Compute Platform Optimization Across Heterogeneous Hardware in a Distributed Computing Environment

ABSTRACT

Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

BACKGROUND

Service providers offer cloud-based services to fulfill users'computing-service needs without the users having to invest in andmaintain computing infrastructure required to implement the services.These service providers maintain networks of managed computing resourcesand functionality to implement various types of scalable, on-demandservices, such as storage services, compute services, database services,networking services, and so forth. The networks of computing resources,or “service provider networks,” can include computing systems that arelocated across multiple, distinct regions and interconnected by acommunication network, or a series of communication networks, toexchange data. Specifically, data centers or data processing centers,may include a number of interconnected computing devices (or “servers”)to provide computing resources to users of the service providernetworks.

To increase the utilization of the computing resources, virtualizationtechnologies may allow a single physical computing device to hostmultiple virtual computing resources. For example, a single computingdevice can host multiple instances of virtual machines (VM) (alsoreferred to herein as “virtual machine instances” or “VM instances”)that appear and operate as independent physical computing devices forusers, but each share or are allocated portions of the computingresources of the single, underlying physical computing device. In thisway, rather than having a single user or process underutilize theresources of a physical computing device, multiple users or processescan utilize the resources of the physical computing device to increaseresource utilization.

To further increase the utilization of the computing resources, and alsoto more effectively meet the computing resource needs of users, serviceprovider networks may offer a variety of different types of virtualmachines. Specifically, a service provider network may offer a selectionof VM instance types that are optimized, or biased, to support differentuse cases on behalf of users. In such examples, the different VMinstance types may be allocated different amounts, and/or differentcombinations, of the computing resources of underlying physicalcomputing devices to provide users with flexibility to choose a VMinstance that is more appropriately optimized to support their computingresource needs.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth below with reference to theaccompanying figures. In the figures, the left-most digit(s) of areference number identifies the figure in which the reference numberfirst appears. The use of the same reference numbers in differentfigures indicates similar or identical items. The systems depicted inthe accompanying figures are not to scale and components within thefigures may be depicted not to scale with each other.

FIG. 1 illustrates a system-architecture diagram of an exampleenvironment in which an optimization service of a service providernetwork determines VM instance types that are optimized to supportworkloads on behalf of users.

FIG. 2 illustrates a component diagram of example components of aservice provider network that help optimize the selection,configuration, and utilization of VM instance types to support workloadson behalf of users.

FIG. 3 illustrates a graphical user interface through which a user of aservice provider network can define a workload to launch on a VMinstance.

FIG. 4. illustrates a system-architecture diagram of a service providernetwork that utilizes historical-utilization data from VM instances andworkloads across user accounts to generate workload categories andresource-utilization models.

FIG. 5 illustrates a graphical user interface through which a user of aservice provider network can review recommendations regarding VMinstance types that are optimized to support their workload.

FIGS. 6A and 6B collectively illustrate a flow diagram of an examplemethod for a service provider network to receive a definition of aworkload from a user account through one or more user interfaces,mapping the workload to a predefined workload category, and provide theuser account with a recommendation as to a VM instance type to supporttheir workload.

FIG. 7 illustrates a flow diagram of an example method for a serviceprovider network to receive a resource-utilization characteristic for aworkload, and provide a recommendation to a user account of a VMinstance type that is to be used to launch the workload.

FIG. 8 illustrates a flow diagram of an example method for a serviceprovider network to receive input via one or more user interfaces thatindicates a resource-utilization characteristic associated with aworkload, determines a VM instance type based on theresource-utilization characteristic, and provides a recommendation to auser account for the VM instance type to support the workload.

FIG. 9 illustrates a system-architecture diagram of a service providernetwork that simulates workloads on new VM instance types to determineperformance of the new VM instance type, and tests workloads on VMinstances before migrating fleets of workloads onto new VM instancetypes.

FIG. 10 illustrates graphical user interface through which a user of aservice provider network can review recommendations to migrate theirworkload to a new VM instance type that is optimized to support theirworkload.

FIG. 11 illustrates a flow diagram of an example method for determiningthat a new VM instance type is more optimized to support a workload thana current VM instance type, recommending the new VM instance type to auser account associated with the workload, and migrating the workload tothe new VM instance type.

FIG. 12 illustrates a flow diagram of an example method for determiningthat the resource utilization of a workload changed, identifying a newVM instance type that is more optimized to host the workload, andproviding a user account with a recommending to migrate the workload tothe new VM instance type.

FIG. 13 illustrates a flow diagram of an example method for determiningthat a new VM instance type is more optimized to support a workload thana current VM instance type, and recommending the new VM instance type toa user account associated with the workload.

FIG. 14 illustrates a system-architecture diagram of an exampleenvironment in which an optimization service of a service providernetwork receives configuration data from an agent executing on a VMinstance that supports a workload, and recommends that modifications bemade to a configuration parameter of the workload or operating system.

FIG. 15 illustrates a flow diagram of an example method for deploying asoftware agent to a VM instance that is hosting a workload, receivingconfiguration data from the agent, and providing a recommendation to auser account indicating a modification to be made to a configurationparameter of an application stack of the workload.

FIG. 16 illustrates a flow diagram of an example method for receivingconfiguration data that specifies a configuration parameter of at leastone of an application stack or an operating system, and either providinga recommendation to a user account indicating a modification to be madeto a configuration parameter or automatically modifying theconfiguration parameter.

FIG. 17 illustrates a system-architecture diagram of an exampleenvironment in which an optimization service of a service providernetwork receives utilization data indicating resource consumption byworkloads on different computing devices, and maps the computing devicesto physical resource consumed to determine performance metrics for thecomputing devices.

FIG. 18 illustrates a flow diagram of an example method for using aperformance ratio between computing devices to determine that acomputing device has performance metrics such that, if a workload ishosted on the computing device, the resulting resource utilization rateof the workload will be within a desired utilization rate.

FIG. 19 illustrates a flow diagram of an example method for determininga hardware device has performance metrics such that the hardware deviceis optimized to host a workload.

FIG. 20 illustrates a system-architecture diagram of an exampleenvironment in which an optimization service of a service providernetwork determines computationally compatible VM instance types, anddeploys the computationally compatible VM instance types on a samecomputing device.

FIG. 21 is a system and network diagram that shows an illustrativeoperating environment that includes data centers of a service providernetwork that can be configured to implement aspects of the functionalitydescribed herein.

FIG. 22 is a computing system diagram illustrating a configuration for adata center that can be utilized to implement aspects of thetechnologies disclosed herein.

FIG. 23 is a computer architecture diagram showing an illustrativecomputer hardware architecture for implementing a computing device thatcan be utilized to implement aspects of the various technologiespresented herein.

FIG. 24 illustrates a flow diagram of an example method for deployingworkloads on VM instances that are supported by a same physical serverbased on the workloads being computationally compatible.

FIG. 25 illustrates a flow diagram of an example method for determiningthat workloads are computationally compatible, and using VM instances ona same hardware resource to host the workloads.

FIG. 26 illustrates a flow diagram of an example method for determiningto place workloads on VM instances that are on a same hardware devicebased on the workloads belonging to computationally compatible workloadcategories.

DETAILED DESCRIPTION

Service providers offer various network-based (or “cloud-based”)services to users to fulfill computing needs of the users. These serviceproviders may operate service provider networks that include clusters ofmanaged servers (or other hardware-based computing devices) stored indata centers located across different geographic regions. A user of theservice provider network can request that the service provider allocatecomputing resources in these data centers to support computing workloadson behalf of the users. One or more services of the service providernetwork can receive these requests and allocate physical computingresources to support the workloads, such as usage of computerprocessors, memory, storage drives, computer network interfaces, and/orother hardware resources of a computing device, for the user.

As noted above, the service provider networks may utilize virtualizationtechnologies such that the computing devices can each host multiple VMinstances that appear and operate as independent computing devices tosupport workloads of users. Rather than allocating all of the computingresources of a physical computing device to support a single workloadfor a user, the computing resources of a physical computing device canbe allocated amongst multiple VM instances that support differentworkloads. The service provider network supports many different types ofworkloads on behalf of users, and these workloads often have differentcomputing resource needs. As described herein, a workload is implementedby a designated set of computing resources and the workload itself canbe considered as code or logic that performs functionality using thecomputing resources. The service provider network may support a widevariety of workloads, such as web servers, databases, customer-facingapplications, distributed data stores, batch processing, machine/deeplearning training and/or inference, online gaming, video encoding,memory caching, and/or any other type of workload that can be supportedby computing resources of a service provider network.

In light of the different workloads that are supported on behalf ofusers, the service provider network may provide users with a selectionof a variety of VM instance types optimized to support differentworkloads. Generally, each VM instance type may be allocated a differentamount of computing resources, and/or different combination of computingresources, such that the VM instance types are optimized, orcomputationally biased, to support different workloads. As used herein,computing resources refers to compute, memory, storage, networking, and,in some implementations, graphics processing. As an example, one VMinstance type may be allocated a larger amount of compute (e.g.,processor cycles) and be optimized to support compute-heavy workloads,whereas another VM instance type may be allocated a larger amount ofstorage (e.g., disk space) and be optimized to support storage-intensiveworkloads. In this way, users can select a VM instance type or platformthat is more optimized to support their workload, thereby increasing theperformance of the workload while reducing underutilization of computingresources by the service provider network.

Generally, an increase in the complexity and diversity of VM instancetypes offered by the service provider network is advantageous andresults in a higher likelihood that workloads are supported by a moreoptimized VM instance. While a large variety of VM instance types isadvantageous for various reasons (e.g., efficient utilization ofcomputing resources, high performance for workloads, etc.), it also maybecome difficult for users, particularly new users, to select a suitableor appropriate VM instance type to support their workload(s). Forexample, users may attempt to map out the computing resource needs oftheir workload and then peruse the offering of VM instance types tolocate a VM instance type that seems appropriate for their needs. Inother examples, users may go through a time-consuming trial-and-errorprocess to analyze performance of their workloads using different VMinstance types. However, not only is this time consuming, but it mayalso result in users having their workloads hosted on VM instance typesthat are either overutilized and resource constrained, or underutilizedand resulting in computing resources that may be unused and sittingidle. As an example, users may be overly cautious and select anoversized VM instance type to help ensure that their workloads are neverresource constrained, which may result in low utilization of computingresources of the service provider network.

This disclosure describes techniques and technologies implemented by anoptimization service of a service provider network to help optimize theselection, configuration, and utilization of VM instance types tosupport workloads on behalf of users. The optimization service mayimplement the techniques described herein at various stages in a lifecycle of a workload to help optimize the performance of the workload,and reduce underutilization of computing resources. For example, theoptimization service may perform techniques to help new users select anoptimized VM instance type on which to initially launch their workload.Further, the optimization service may be configured to monitor aworkload for the life of the workload, and determine different VMinstance types, and/or different configuration modifications, thatoptimize the performance of the workload. In this way, the optimizationservice may provide recommendations to users that help improveperformance of their workloads, and that also increase the aggregateutilization of computing resources of the service provider network.

The optimization service may perform techniques to help new users selecta VM instance type that is optimized to host or support their workload.Often new users may be unsophisticated with respect to computingresources, and/or unfamiliar with certain terminology. Accordingly, theoptimization service may deliver a managed experience, such as astep-by-step process, that allows new users to describe their workloadusing language and terminology that the new user understands, and thenprovides the user with recommendations for VM instance type(s) optimizedfor their workload. In some examples, the optimization service mayinclude a wizard that is accessible to a new user via their user accountand presents user interfaces to the user that are configured to receiveinput data that defines that user's workload. The wizard may presentuser interface(s) that include text-input fields to receive a textualdescription of a workload from a user, or fields with a drop-down menuthat include answers for a question regarding the workload of the user,such that a user can answer high-level questions about their workloads.For example, the wizard may present the user with questions such as “isyour workload a publicly facing website,” or “how many visitors do youexpect per day?”. The optimization service may use the input receivedfrom the user to classify the workload as belonging to a predefinedworkload category (e.g., web-server category, database category,compute-heavy category, etc.), and provide the user account with alisting of recommended VM instance types to support their workload. Thelisting may further include explanations regarding why the VM instancetypes are optimized for their workload to enable the user to make a moreinformed decision regarding what instance type they should select. Theuser may then select a VM instance type to support their workload, andthe optimization service may perform further techniques for launchingone or more VM instances of the selected type to support the workload onbehalf of the user account. Thus, the optimization service may beconfigured to identify and recommend VM instance types for new workloadsand/or new users.

Additionally, the optimization service may be further configured to helpoptimize the performance of the workload for the life of the workload.In some instances, and regardless of whether the optimization servicepreviously identified and/or recommended VM instance types for newworkloads or new users, the optimization service may determine that theworkload would be better suited on a different VM instance type. Forexample, the workload may have changed over time (e.g., software update,new features, increase in user traffic, etc.) that in turn results indifferent resource-consumption characteristics of the workload. In lightof such modifications or changes, the optimization service maycontinually, or periodically, analyze the resource-utilizationcharacteristics of the workload and determine if resource consumptionhas changed significantly enough such that a new VM instance type ismore appropriate for the workload than the current VM instance type. Inother examples, the service provider network may develop and offer newVM instance type(s) to increase the offerings of VM instance types forusers. The optimization service may use various techniques, such asworkload simulation, to determine that the new VM instance type is moreoptimized for the workload (or workload category to which the workloadbelongs) than the currently utilized VM instance type. For such reasons,and potentially other reasons, the optimization service may provide theuser account with recommendations that the user migrate their workloadfrom the current VM instance type to be hosted by a different VMinstance type that is more optimized for the resourceconsumption/utilization of the workload.

To determine a VM instance type that is optimized for a workload, theoptimization service may have generated a predefined set of workloadcategories that generally represent or group the workloads supported bythe service provider network into categories based on the “shape” of theutilization characteristics of the workloads. The shape of utilizationcharacteristics can refer to the amount of usage across each differentcompute dimension—processing, memory, storage, networking, andoptionally graphics processing—which may be visualized as a plot havinga number of axes corresponding to the number of compute dimensions. Theplotting of utilization along each axis can result in a specific shape,for example a quadrilateral or other polygon formed by connecting theplotted points. This may be a static shape representing an average ormean utilization, a set of shapes representing minimum, maximum,average, or other statistical analyses of utilization over time, or adynamic shape representing utilization across the compute dimensionsover time. Certain utilization shapes (or ranges of similar utilizationshapes) may be determined (manually or by application of suitablemachine learning analysis) to represent particular types of workloads.For example, the optimization service may have collectedresource-utilization data for workloads that are supported by theservice provider network, and based on the resource-utilizationcharacteristics (or “resource-consumption characteristics”) of theworkloads, performed techniques, such as clustering, to group theworkloads into categories based on the shape of their resourceutilization. To illustrate, one predefined workload category may be a“database workload category” and generally correspond to theresource-utilization characteristics for database workloads (e.g., lowcompute consumption, high storage consumption, etc.). Another predefinedworkload category may be a “compute heavy category” and generallycorrespond to the resource-utilization characteristics forcomputationally-biased workloads.

Each of the workload categories may be defined by a respectiveresource-utilization model that indicates that shape of theresource-utilization characteristics for the workloads represented bythe workload category. The resource-utilization models may indicateamounts of the different types of resources consumed by therepresentative workloads (e.g., amounts of CPU, storage, memory,networking throughput, GPU, etc.), and or combinations of the differenttypes of resources consumed by the representative workloads. Theworkload categories may further be associated with the VM instance typesthat are optimized for the resource-utilization characteristics of therepresented workloads. In this way, when resource-utilizationcharacteristics are obtained from a description provided by a new user,or through actual utilization data throughout the life of a workload,the resource-utilization characteristics may be mapped to the “closest”resource-utilization model of a predefined workload category, and theassociated VM instance types for that workload category may be providedas recommendations to optimize performance of the workload.

In addition to utilizing resource-utilization characteristics todetermine an optimized VM instance type for a workload, the optimizationservice may further take into account the performance of the underlyingphysical computing devices. Service provider networks may manage largeamounts of computing resources, and in some examples, may includecomputing devices with hardware differences. The service providernetwork may include computing devices that have different chip setgenerations, different vendors, and/or different hardware architecturessuch that actual performance of the computing devices varies based onthe hardware differences. For example, a computing device that has achip set from a newer generation may perform better, or have more datathroughput, than a computing device with a chip set from an oldergeneration of chip sets. Thus, even if a VM instance is provided with,for example, the same number of virtual central processing units (vCPUs)when provisioned on two different computing devices, the performance forone of the VM instances hosting a workload may be better due to hardwaredifferences (or improvements) in the physical resources of the computingdevices. To help account for performance differences that result fromphysical hardware differences, the optimization service may maputilization data back to the underlying physical computing resource thatis consumed to get a performance metric for that computing device. Inthis way, performance metrics may be assigned to the underlyingcomputing device on which a VM instance is provisioned to help determinean optimized VM instance type based on the computing device that is tobe utilized. In an example where a workload is migrated from a lesscompute-performant device onto a more compute-performant device, theoptimization service may select a new VM instance type based in part ona ratio of the performance between the two devices. In this way, theoptimization service may select a new VM instance type that may not needbe allocated as much compute power of the more compute-performancecomputing device.

Additionally, the optimization service may be configured to collectvarious data regarding an application stack of the workload and/or anoperating system of the VM instance, and determine modifications to maketo configuration parameters of the application stack and/or operatingsystem to help optimize the performance of the workload. For example,the optimization service may infer various configuration data about theworkload based on utilization characteristics, such as inferring thatthe workload is a database based on the utilization characteristics ofthe workload mapping to a workload category for databases. In someinstances, the optimization service may, with permission from the useraccount, install a software agent locally to the VM instance(s) thatsupport the workload, and collect configuration data using the softwareagent. The optimization service may receive configuration dataindicating information about the application stack and/or operatingsystem, such as what processes are running, what binary backs theprocesses, what repositories those binaries are sourced from, whatoperating system and/or version is running, what configurationparameters are defined for the operating system, disk subsystem,database stack, etc., and/or any other configuration data. Theoptimization service may then determine modifications for a parameter ofat least one of the application stack and/or operating system tooptimize performance of the workload, and provide recommendationsindicating the modifications for the parameter. The user may thenutilize their user account and accept or deny the proposed modificationsto optimize their workload.

In addition to selecting a VM instance type that is optimized for theworkload, the optimization system may also intelligently place the VMinstance type on a computing device based on computational biases of theVM instance types. As noted above, the different types of VM instancetypes may have different computational biases based on the workloadsthey support, such as CPU biases, memory biases, network throughputbiases, and so forth. Rather than placing virtual machines with similarcomputational biases on the same physical computing devices, theoptimization service may determine complimentary combinations of VMinstance types based on their computational biases, and place thosecomplimentary combinations on the same servers. For example, theoptimization service may place a VM instance type that utilizes high CPUresources, but low memory resources, on the same computing device asanother VM instance type that utilizes low CPU resources, but highmemory resources. In some examples, the optimization service may storeindications of complimentary pairs, or complimentary combinations, of VMinstance types that are determined to be complimentary based on theresource-utilization models of the workload categories to which the VMinstance types are associated or belong. In this way, the underlyingcomputing resources of the computing devices may be more efficiently andeffectively utilized by intelligently hosting different VM instancetypes that are complimentary in how they utilize computing resources.

Although the techniques described herein are described primarily withrespect to determining a VM instance type for a workload, andprovisioning a VM instance to support the workload, the techniques areequally applicable for any number of VM instances and/or workloads. Forexample, a workload may be supported by a VM instance, by multiple VMinstances, and/or by a fleet of VM instances. In some examples, one ormore workloads may be supported by a fleet of VM instances that arescalable to support increases and decreases in use, and may be placedbehind one or more load balancing devices of the service providernetwork. In such examples, the techniques described herein may beapplicable to all VM instances in a fleet that support various instancesof the same workload.

To provide users more control over their workloads and VM instancetypes, the optimization service may simply provide recommendations viauser accounts that the users should consider new VM instance typesand/or configuration parameters to optimize performance of theirworkload. However, in some examples the optimization service may beconfigured to automate the migration of workloads to new VM instancetypes, and/or implementation of new configuration parameters. Forexample, the users may select an option, or “opt in,” to give theoptimization service permission to automate the migration of workloadsto new VM instance types.

In some examples, prior to recommending or automating the migration ofworkloads to new VM instance types, or the modification of configurationparameters, the optimization service may test the recommended changes onone or more “test” VM instances. That is, the optimization service maydesignate, or spin up, a VM instance type that is determined to be moreoptimized for a workload than a current VM instance type. Theoptimization service may then cause a workload to be hosted or supportedby the proposed VM instance type and monitor the health or performanceof the workload. If the workload does in fact perform better on theproposed VM instance type compared to the current VM instance type, thenthe optimization service may move forward with providing arecommendation to a user account, and/or automating the migration of theworkloads for the user to the new VM instance type.

This application describes techniques that increase the overallutilization of computing resources provided by servers or other hardwaredevices, such as CPU, GPU, memory, disk, and/or network availability.The optimization service may determine VM instance types that are moreappropriately tailored, or allocated a more appropriate amount ofcomputing resources, to support for workloads. In this way, thetechniques described herein help prevent underutilization of computingresources of a service provider network, which reduces the amount ofcomputing resources that are (i) allocated or reserved for VM instances,but (ii) sit idle or unused because the VM instances are oversized forthe workload they support. Additionally, the techniques improve theperformance of workloads by intelligently placing workloads on VMinstance types that are computationally biases or optimized to supportthe workloads. The optimization service may place the workloads on VMinstances to help ensure that the workloads have sufficient amounts ofcomputing resources available, of the types of computing resourcesneeded, to help avoid over constrained VM instance types and workloads.

Although the techniques described herein are with reference to virtualmachines or VM instances and virtual machine types, in some examples,the techniques are applicable to any type of virtual computing resource.For example, the techniques are generally applicable to any type ofvirtual computing resource that is allocated underlying portions ofphysical computing resources and executes within a virtual machine, orindependently executes on the physical computing resources. Such virtualcomputing resources can include a container executing on a physicalresource, a virtual machine instance running one or more containers,processes, software, and/or any other executable that is allocatedportions of physical computing resources.

Certain implementations and embodiments of the disclosure will now bedescribed more fully below with reference to the accompanying figures,in which various aspects are shown. However, the various aspects may beimplemented in many different forms and should not be construed aslimited to the implementations set forth herein. The disclosureencompasses variations of the embodiments, as described herein. Likenumbers refer to like elements throughout.

FIG. 1 illustrates a system-architecture diagram of an exampleenvironment 100 in which an optimization service of a service providernetwork determines VM instance types that are optimized to supportworkloads on behalf of users.

As illustrated, a service provider network 102 may be operated and/ormanaged by a service provider 104. The service provider network 102 mayprovide various services to users 105 to fulfil their computing resourceneeds, such as cloud-based computing resources. For example, the serviceprovider network 102 may provide cloud-based, scalable, and networkaccessible compute power services, storage services, database services,and/or other services. Users 105 may utilize user devices 108 tosubscribe for use of the computing resources and/or services provided bythe service provider network 102. The service provider network 102 mayinclude an optimization service 106 that is configured to select VMinstance types to support workloads of the users 105 which optimizeperformance of the workloads, and refrain from underutilization oroverutilization of the computing resources that support the VM instancesand workloads.

The service provider network 104 may span across different geographicregions, and include or be associated with a computing resource network110 that includes clusters of managed computing devices 112 (e.g.,servers) stored in data centers located across the different geographicregions. In this way, users 105 who have subscribed for use of thenetwork-based services supported by computing resources in the datacenters 116 need not invest in and maintain the computing infrastructurerequired to implement the various services that they may need. In someexamples, users 105 of the service provider network 102 may access orutilize computing resources of the computing devices 112 in the datacenters located in different geographic regions such that users 105located in these different geographic regions are provided with accessthese resources and services.

Generally, the computing devices 112 may provide various types ofcomputing resources, such as compute (CPU) resources (e.g., centralprocessing units (CPUs) for processing data), memory resources (e.g.,physical devices capable of storing information such as RAM or ROM),storage resources (e.g., disk storage or drive storage used to storedata by various electronic, magnetic, optical, or mechanical changes toa surface layer of one or more rotating disks), graphics compute (GPU)resources (e.g., graphics processing units (GPUs)), and/or networkthroughput resources (e.g., average or measured rate of bit transmissionper second over networks). The computing devices 112 may be varioustypes of computing devices, such as devices that have different chip setgenerations, are from different vendors, have different hardwarearchitectures, and so forth.

Thus, the computing resources of the computing-resource network 110provided by the computing devices 112 can include, for example, anyhardware computing device resources, such as processor computingpower/capacity, read-only and/or random-access memory, data storage andretrieval systems, device interfaces such as network or peripheraldevice connections and ports, and the like. In some embodiments, theseresources may be dispersed among multiple discrete hardware computingdevices (e.g., servers), and these hardware computing devices 112 mayimplement or communicate with a virtualization layer and correspondingvirtualization systems (e.g., a hypervisor on a server), whereby thecompute resources are represented by, and made accessible as, virtualcomputing resources, such as instances of virtual machine or “VMinstances.” A virtual computing resource may be a logical construct,such as a data volume, data structure, file system, and the like, whichcorresponds to certain compute resources. Non-limiting examples ofvirtual computing resources include virtual machines and containers (asdescribed below), logical data storage volumes capable of storing filesand other data, software programs, data processing services, and thelike.

As illustrated, the computing devices 112 may each support VM instancesthat may be different types of VM instances provided by the serviceprovider network 102. For instance, computing devices 112(1) may supportone or more VM instances 114(1)-114(N) that are of a first VM instancetype, and computing devices 112(2) may support one or more VM instances116(1)-116(N) that are of a second VM instance type. Rather thanallocating all the computing resources of an entire computing device 112to support a workload for the user 105, the service provider network mayinclude a virtualization layer (e.g., containing one or morehypervisors) that includes instances of “virtual” computing resources(also referred to interchangeably herein as “virtual machines” or “VMinstances”) that represent the allocated portions of the physicalcomputing resources of the computing devices 112. These VM instances114/116 may emulate computing devices 112 to operate and supportworkloads, and may have their own operating systems, processingcapabilities, storage capacity, and network connections or interfaces.

Users 105 may create user accounts with the service provider 104 toutilize the resources and services of the service provider network. Theusers 105 may utilize their user devices 108 to communicate over one ormore networks 118 (e.g., WANs, PANs, LANs, etc.) with the serviceprovider network 102. The user devices 106 may comprise any type ofcomputing device configured to communicate over network(s) 118, such asmobile phones, tablets, laptop computers, desktop computers,televisions, servers, and/or any other type of computing device. Theusers 105 may desire that the service provider network 102 host orsupport workloads on the computing resource network 110 that is managedby the service provider 104. Accordingly, the users 105 may, via theiruser account, request that a workload be launched on their behalf, andprovide workload data 120 via one or more user portals 122 (e.g., webconsole, command line interface (CLI), application programming interface(API), etc.). The user portals 122 may provide the workload data 120 tothe optimization service 106 which includes a recommendation engine 124,an optimization component 126, and a VM instance type library 128storing indications of different VM instance types 130(1)-130(N) offeredby the service provider network.

As described herein, a workload 136 may generally include a designatedcollection or grouping of computing resources (e.g., compute, memory,storage, networking, etc.) in the computing-resource network 110, andthe code or logic that performs functionality using the computingresources. The service provider network 102 may support a wide varietyof workloads 136, such as web servers, databases, customer-facingapplications, distributed data stores, batch processing, machine/deeplearning training and/or inference, online gaming, video encoding,memory caching, and/or any other type of workload that can be supportedby computing resources of the computing-resource network 110.

The user 105 may provide workload data 120 that generally indicates oneor more resource-utilization characteristics of the workload 136 that isto be hosted or supported on behalf of the user's account. In someinstances, the optimization service 106 may provide a wizard that isaccessible to the user 105 via their user account and presents, via theuser portal(s) 122, user interfaces to the user device 108 that areconfigured to receive the workload data 120 that defines that user'sworkload 136. The wizard may present user interface(s) that includetext-input fields to receive a textual description of a workload from auser, or fields with a drop-down menu that include answers for aquestion regarding the workload of the user, such that a user can answerhigh-level questions about their workloads. Further description of thewizard is found below with respect to at least FIG. 3.

In other examples, the user 105 may have previously hosted theirworkload 136 using on-premise computing resources, or other managedcomputing resources, and obtain actual resource-utilizationcharacteristics that indicate the amount and types of computingresources utilized by the workload 136. In such examples, the user 105may provide the actual resource-utilization data as part of the workloaddata 120 to the service provider network 102.

The optimization service 106 includes the optimization component 126that is configured to determine one or more VM instance types 130 thatare optimized to support the workload 136 on behalf of the user 105. Theservice provider 102 may offer a wide variety of VM instance types 130that differ based on (i) the amounts of physical computing resourcesallocated for use by the VM instance type 130, and/or (ii) thecombinations of the types of physical computing resources allocated foruse by the VM instance type 130. In some instances, there may be atleast five high-level categories or types of computing resourcesincluded in the computing-resource network 110 and provided by thecomputing devices 112, which are CPU, GPU, memory, storage, and networkthroughput. The different VM instance types 130 are allocated differentamounts and/or combinations of these, and potentially other, computingresources. For example, the VM instance types 130 may be allocated useof larger or smaller amounts of the different resource types to becomputationally biased or optimized support workloads 136 with variouscomputing resource utilization characteristics.

For example, the VM instance types 130 can include compute optimizedtypes, memory optimized types, accelerated optimized types, storageoptimized types, and/or network throughput optimized types. As aspecific example, a VM instance type 130 that is compute optimized maybe allocated use of 4 vCPUs of 3.0 GHz processors where each core canrun at up to 3.5 GHz, but only be allocated 8 gibibytes (GiB) of memory.Conversely, a VM instance type 130 that is memory optimized may beallocated 32 GiB of memory, but only run on a 3.1 GHz processor with 2vCPUs.

In addition to biasing the VM instance types 130 by varying the amountsor ratios of computing resource types allocated for use by the differentVM instance types 130, the service provider 104 may further includedifferent sizes of VM instance types 130 for workloads 136 that requiremore or less computing resources at various ratios. For example, asmaller VM instance type 130 that is computationally biased may beallocated 2 vCPUs of a 3.0 GHz processor and 4 GiB of memory, and anextra-large VM instance type 130 that is computationally biased may beallocated 72 vCPUs on the 3.0 GHz processor and 144 GiB of memory (e.g.,36× the vCPUs and memory allocation of the smaller type).

Accordingly, the service provider 104 may offer a wide selection of VMinstance types 130 that are included in a VM instance type library 128in which a user 105 can search and select a desired VM instance type 130for their workload 136. Traditionally, the users 105 would have tomentally map out the computing resource needs of their workload 136 andperuse the library 128 offering of VM instance types 130 to locate a VMinstance type 130 that seems appropriate for their needs. However, notonly is this time consuming, but it may also result in users 105 havingtheir workloads hosted on VM instance types 130 that are eitheroverutilized and resource constrained, or underutilized and resulting incomputing resources that may be unused and sitting idle.

The optimization component 126 may be configured to determine one ormore VM instance types 130 that are optimized to host or support theworkload 136. For example, the optimization component 126 may generallymap the workload data 120 (e.g., resource-utilization data, descriptionof the workload 136, etc.) to one or more VM instance types 130 that arecomputationally biases, or optimized, to support the resourceutilization of the workload 136. In some examples, and described in moredetail with respect to FIG. 2, the optimization component 126 maygenerate predefined workload categories or groups that generallyrepresents higher-level categories of workloads 136 commonly hosted onthe computing-resource network 110. For example, one workload categorymay be a database category and represent different database workloadssupported by the computing-resource network 110. Another category may bea web-server category and represent the different web-server workloadssupported by the computing-resource network 110. The optimizationcomponent 126 may analyze the different types of workloads 136 supportedacross the computing-resource network 110 on behalf of the user accountsand define (e.g., machine learning, clustering, etc.) a set of workloadcategories that are generally representative of the different workloads136 supported by the computing-resource network 110.

Further, the optimization component 126 may determine one or moreresource-utilization models for each workload category that representthe general “shape” or characteristics of the resource utilization bythe workloads 136 represented in each category. That is, each workloadcategory may be associated with one or more resource-utilization modelsthat are generally representative of the resource consumption byworkloads 136 in the workload category. The optimization component 126may further determine, based on the resource-utilization models (or byuser account selection) which of the VM instance types 130 arecomputationally biased or optimized for the different workloadcategories. As an example, VM instance type 130 that are computeoptimized may be associated with a high-performance web server workloadcategory, whereas a VM instance type 130 that is memory optimized may beassociated with a higher-performance database category. In this way,workload categories may be generated or predefined that arerepresentative of the resource-utilization characteristics for theworkloads 136 that are supported by the computing-resource network, andalso indicate the VM instance types 130 that are optimized or biased tosupport the workloads for each workload category.

The optimization component 126 may map the workload data 120 to at leastone of the predefined workload categories in various ways. For instance,the workload data 120 may include one or more words that describe theresource-utilization data of the workload 136, such as “web server,”“database,” “compute heavy,” and so forth. In some examples, theoptimization component 126 may simply map actual utilization data of theworkload 136 to a workload category in instances where the user 105 ismigrating the workload 136 from a remote computing-resource network ontothe computing-resource network 110. After the optimization component 126maps the workload data 120 to one of the predefined workload categories,the recommendation engine 124 may provide recommendation data 132 to theuser device 108 that includes at least a recommendation of a VM instancetype 130 that is optimized to support their workload 136.

The recommendation engine 124 may determine one or more of the VMinstance types 130 associated with the workload category, and mayfurther rank the VM instance types 130 based on how strongly theworkload data 120 corresponds to one of the VM instance types 130 forthat workload category. Depending on the size (e.g., amount ofresources), and/or the combination of computing resources, for theworkload 136, the recommendation engine 124 may provide a ranked listingof VM instance types 130 that are recommended for the workload data 120.In some examples, the recommendation engine 124 may further providesuitability data that indicates how suitable the recommended VM instancetypes 130 are for supporting the workload 136, such as indicating anumber of stars out of five stars, percentages indicating how suitableout of one-hundred percent, and/or any other suitability score orindicator. Further, the recommendation engine 124 may provide a textualexplanation regarding why the VM instance types 130 are optimized tosupport the workload 136 such that the user 105 may make a moreintelligent decision as to which of the VM instance types 130 they wouldlike to launch their workload 136 on. The VM instance recommendations142 may be presented in a dashboard 140 accessible via the userportal(s) 122, and the user 105 may select the VM instance type 130 onwhich they would like to launch their workload 136.

The optimization service 106 may receive input data indicating aselection of the a recommended VM instance type 130, and provide acompute-management service 134 an instruction to launch the workload 136on one or more (e.g., a fleet) of VM instances 114 that correspond tothe VM instance type 130 that the user 105 selected. In some examples,the workload 136 may include code provided by the user 105, and/orgenerated by the service provider network 102, to implementfunctionality of the desired workload 136. For example, the serviceprovider network 102 may provide services that generate code for theworkload 136, including an application stack and/or other programs, toimplement the workload 136. The workload 136 may be supported by one VMinstance 114, and/or a fleet of VM instances 136. In some examples, oneor multiple VM instances 114 in a fleet of VM instances 114 may supportrespective workloads 136 on behalf of the user account of the user 105.The compute-management service 134 may further deploy one or more loadbalancers in front of the fleet of VM instances 114 to scale theworkload(s) 136, and other configurations or devices (e.g., securitygroups) to support the workload. In this way, the optimization service106 may help a user 105 select, configure, and utilize a VM instancetype 130 that is optimized to support a new workload 136 for the user's105 account.

In some examples, the optimization service 106 may further monitor theworkload 136 for the life of the workload 136, and provide additionalrecommendation data 132 upon detecting events that result in a differentVM instance type 130 being more optimized to support the workload thanthe current VM instance type 130 to which the VM instance 114corresponds. For instance, the user 105 may provide an indication to theoptimization service 106 that the workload 136 has undergone aconfiguration change (e.g., update, software change, traffic change,etc.) that will likely result in a change in the resource-utilizationcharacteristics of the workload 136. In other examples, the optimizationservice 106 may periodically, or continuously, collectresource-utilization data 138 from the VM instance 114 that indicates achange in the resource-utilization characteristics of the workload 136.

In light of such modifications or changes, the optimization service 106may continually, or periodically, analyze the resource-utilization data138 of the workload 136 and determine if resource consumption haschanged such that a new VM instance type 130 is more appropriate for theworkload 136 than the current VM instance type 130 (e.g., VM instance114). In other examples, the service provider 102 may develop and offernew VM instance type(s) 130 to increase the offerings of VM instancetypes 130 for users 105. The optimization service 106 may use varioustechniques, such as workload simulation, to determine that the new VMinstance type 130 is more optimized for the workload 136 (or workloadcategory to which the workload 136 belongs) than the currently utilizedVM instance type 130. For such reasons, and potentially other reasons,the optimization service 106 may provide the user account of the user105 with additional recommendation data 132 that includes arecommendation for the user 105 migrate their workload 136 from thecurrent VM instance type 130 (e.g., VM instance 114) to be hosted by adifferent VM instance type 130 (e.g., VM instance 116) that is moreoptimized for the resource consumption/utilization of the workload 136.

In such examples, the optimization service 106 may provide aninstruction to the compute-management service 134 to migrate theworkload 144 to be hosted on one or more VM instances 116(1)-(N) thatcorrespond to the VM instance type 130 that was determined to be moreoptimized for the workload 136.

Generally, the optimization service 106, and components thereof, maycomprise software, firmware, and/or other logic that is supported onecomputing device, or across more computing devices in the serviceprovider network 102. Additionally, the optimization service 106 maycomprise a system of other devices, such as software agents storedlocally on VM instances 114/116.

FIG. 2 illustrates a component diagram 200 of example components of aservice provider network 102 that help optimize the selection,configuration, and utilization of VM instance types 130 to supportworkloads 130 on behalf of users 105.

As illustrated, the service provider network 102 may include one or morehardware processors 202 (processors), one or more devices, configured toexecute one or more stored instructions. The processor(s) 202 maycomprise one or more cores. Further, the service provider network 102may include one or more network interfaces 204 configured to providecommunications between the service provider network 102 and otherdevices, such as the user device(s) 108, computing devices 112, and/orother systems or devices in the service provider network 102 and/orremote from the service provider network 102. The network interfaces 204may include devices configured to couple to personal area networks(PANs), wired and wireless local area networks (LANs), wired andwireless wide area networks (WANs), and so forth. For example, thenetwork interfaces 204 may include devices compatible with Ethernet,Wi-Fi, and so forth.

The service provider network 102 may also include computer-readablemedia 206 that stores various executable components (e.g.,software-based components, firmware-based components, etc.). In additionto various components discussed in FIG. 1, the computer-readable-media206 may further store components to implement functionality describedherein. While not illustrated, the computer-readable media 206 may storeone or more operating systems utilized to control the operation of theone or more devices that comprise the service provider network 102.According to one embodiment, the operating system comprises the LINUXoperating system. According to another embodiment, the operatingsystem(s) comprise the WINDOWS SERVER operating system from MICROSOFTCorporation of Redmond, Wash. According to further embodiments, theoperating system(s) can comprise the UNIX operating system or one of itsvariants. It should be appreciated that other operating systems can alsobe utilized.

Additionally, the service provider network 102 may include a data store208 which may comprise one, or multiple, repositories or other storagelocations for persistently storing and managing collections of data suchas databases, simple files, binary, and/or any other data. The datastore 208 may include one or more storage locations that may be managedby one or more database management systems.

The computer-readable media 206 may store portions, or components, ofthe optimization service 106 described herein. For instance, thecomputer-readable media 206 may store a service endpoint 210 that mayinclude a stack that supports internet routable APIs to describe,generate, delete, and make recommendations using resource-utilizationdata 138 or characteristics. Generally, this service stack of theservice endpoint 210 may support APIs, CLI, consoles, SDKs, and/or anyother function through which the components of the optimization servicecall, and/or the user devices 108.

The computer-readable media 206 may further store the user portal(s) 122through which users 105 can provide input via their user accounts anduser devices 108. In some examples, the user portal(s) 122 include aninterface through which users 105 can upload resource-utilization data138 from on-premise or other remote computing systems that hosted theirworkload 136. Additionally, the user portal(s) 122 may include theweb-console wizard 212 which presents one or more console userinterface(s) 214 (or UIs 214) through which the users 105 may provideworkload data 120 that defines or describes their workloads 136. Theservice endpoint 210 may receive calls from APIs, CLIs, SDKs, and/orother electronic means or methods.

The computer-readable media 206 may further store a profile generator216 that generates a snapshot of profiling data, such as aresource-utilization characteristic included in the resource-utilizationdata 138, at regular intervals. The profile generator 216 may thenutilize these snapshots to create a resource fingerprint for a workload136, which generally represents the resource consumption of the workload136. These fingerprints or profiles may be included in theresource-utilization data 138 and be mapped to VM instance types 130and/or workload categories for the workload 136. The profile generator216 may further accumulate and average all resource-utilization data 138for a fleet of VM instances 114/116 in order to generate a consumptionfingerprint for a fleet of VM instances 114/116.

The computer-readable media 206 may further store a clustering component218 configured to create or generate the workload categories 220. Asdescribed in more detail with respect to FIG. 4, the clusteringcomponent 218 may obtain historical (or near-real time) utilization data138 and cluster the workloads 136 for some or all of the user accountsof the service provider network 102 to generate the workload categories220 that are generally representative of all the workloads 136 in theservice provider network 102.

The computer-readable media 206 may further store a machine-learning(ML) component 222 configured to generate the resource-utilizationmodels 224 for each of the workload categories 220. The ML component 222may perform various techniques, and utilize various ML algorithms, totrain one or more resource-utilization models 224 that representresource-utilization characteristics representative of the workloads 136in each workload category 220. In this way, when a new workload 136needs to be categorized for purposes of identifying optimized VMinstance types 130, the resource-utilization data 138 for the newworkload 136 may be mapped to the resource-utilization model 224 that is“closest” or “most near” (e.g., neural network models) the fingerprintof the resource-utilization data 138 for the new workload 136. The MLcomponent 222 may utilize any type of ML algorithm or technique to trainthe resource-utilization models 224.

The computer-readable media 206 may further store the optimizationcomponent 126 configured to perform techniques described above formapping resource-utilization data 138 to the appropriate workloadcategories 220, such as machine-learning methods or ruled based methods.For example, the optimization component 126 may compare utilization bythe workload 136 for one or more dimensions of compute (e.g., CPU, GPU,memory, disk, and/or network throughput) with the resource-utilizationmodels 224 to identify closest match across the one or more dimensionsof compute. The optimization component 126 may further determine whichof the VM instance identifiers 226 are associated with the workloadcategories 220, and provide the user(s) 106 with indications of theoptimized VM instance types 130 that are optimized for their workload136.

The computer-readable media 206 may further store the recommendationengine 124 that is configured to generate and provide recommendationdata 132 to the user device 108 to recommend VM instances 114 on whichto initially launch workloads 136, and also to continue to monitor theworkload 136 for the life of the workload 136 and determine if other VMinstance types 130 are more optimized for the workload 136. Therecommendation engine 124 may generate recommendation data 132 includinga VM instance type listing 228 (e.g., ranked list of VM instance types130 based on suitability for the workload 136), suitability/risk scores230 that indicate how suitable or optimized a VM instance type 130 isfor the workload 136, and/a textual explanation 232 that details why aVM instance type 130 is optimized for the workload 136. Therecommendation engine 124 may, if the user 105 opts in for arecommendation, provide recommendation data 132 to the user devices 108to help users 105 select a VM instance type 130 on which to initiallylaunch a new workload 136, and/or as the workload 136 becomes moresuitable to be supported by different VM instance types 130 throughoutthe life of the workload 136.

The suitability/risk scores 230 may indicate various data regarding howsuitable a VM instance type 130 is to support a workload 136. Forexample, the suitability/risk scores 230 may indicate only how suitablea VM instance type 130 is to support a workload 136 using variousnumeric, text-based, and/or other scoring means. In some examples, thesuitability/risk scores 230 may only indicate how risky a VM instancetype 130 is to support a workload 136 using a scoring means (e.g., riskof bottlenecks). In some examples, there may be multiplesuitability/risk scores 230 indicating risk and suitability. In furtherexamples, the suitability/risk scores 230 may be a single scoreindicated a weighting between risk and suitability to indicate anoverall appropriateness of the VM instance type 130 for supporting aworkload 136.

The computer-readable media 206 may further store a simulation component234 that simulates workloads 136 on VM instances 114. For instance,rather than using historical resource-utilization data 138, thesimulation component 234 may simulate consumption by different workloads136 on different VM instance types 130 in order to determine whatworkloads 136 are optimized for what VM instance types 130 (e.g.,throughput compared to allocated computing resources). Additionally, thesimulation component 234 may simulate workloads 136 on new VM instancetypes 130 that have been introduced by the service provider 104 for useby the users 105. For example, the simulation component 234 may simulatethe consumption of different workloads 136 on the new VM instance types130, and determine performance metrics that indicate throughput of datafor amounts of computing resources input into the new VM instances 114.In this way, when a new VM instance type 130 is offered to users 105,the optimization service 106 may still determine what workloadcategories 220, and thus what workloads 136, would benefit from beingmigrated and/or launched on the new VM instance type 130.

The computer-readable media 206 may further store a testing component236 configured to test workloads 136 on VM instances 114 prior tomigrating the workloads 136. For example, the optimization service 106may allocate computing devices 112 to support test VM instances 114.Using these test VM instances 114, the testing component 236 maydetermine whether a workload 136 actually performs well, or is furtheroptimized, when placed on a new VM instance 116 as compared to a currentVM instance 114. For example, the testing component 236 may “spin up” orprovision a VM instance 116 corresponding to a VM instance type 130 thatthe optimization component 126 has determined is optimized for aworkload 136. Prior to recommending the new VM instance type 130 to auser 105, the testing component 236 may first test the workload 136 onthe test VM instance 116 and receive health data 238 indicating how wellthe workload 136 is performing. Based on the health data 238, thetesting component 236 can provide insight to the optimization component126 regarding whether or not the new VM instance type 130 is in factoptimized compared to the current VM instance type 130 for the workload136.

The computer-readable media 206 may further store code for thecompute-management service 134, which may be implemented by one, ormultiple, computing devices 112 of the service provider network 102.Generally, the compute-management service 134 may be a service of theservice provider network 102 that provides secure, resizable computecapacity and manages the computing resources of the computing-resourcenetwork 110. In some examples, the compute-management service 134 mayperform various functions for managing the computing-resource network110, such as provisioning VM instances 114, migrating workloads 136between VM instances 114/116, providing auto-scaling for fleets of VMinstances 114, configuring VM instances 114 and/or workloads 136, and/orperforming any other functions for managing the computing-resourcenetwork 110. In some instances, the compute-management service 134 mayreceive commands from the optimization service 106 for managing theworkloads 136 and/or VM instances 114/116 for users 105 of the serviceprovider network 102.

In some examples, the compute-management service 134 may include anauto-scaling component that, when executed by the processor(s) 202,scales up or down the number of instances 114 available to support oneor more workloads 136. For example, the auto-scaling component mayprovide a fast, efficient, and accurate way to match fleet capacity tousage. In some examples, the auto-scaling component may track thefleet's hosting metrics and determine when to add or remove instances114 based on a set of guidelines, called policies. The auto-scalingcomponent can adjust capacity in response to changes in demand to helpensure that the fleet of instances 114 has availability for burstswithout maintaining an excessive amount of idle resources.

To utilize the services provided by the service provider network 102,users 105 may register for an account with the service provider network102. For instance, users 105 may utilize a user device 108 to interactwith an identity and access management (IAM) component 240 that allowsthe users 105 to create user accounts 242 with the service providernetwork 102. Generally, the IAM component 240 may enable the users 105to manage their workloads 136 and other computing resources securely.Using the IAM component 240, the users 105 may manage their VM instances114 as described herein. Additionally, users 105 may perform variousoperations for interacting with the optimization service 106 via theiruser accounts 242, such as providing workload data 120, receivingrecommendation data 132, proving input data indicating selections of VMinstance types 130, and/or other interactions may be authorized viacredentials required to access the user accounts 242.

The computer-readable media 206 may be used to store and retrieveinformation, such as program modules, data structures, or other data. Itshould be appreciated by those skilled in the art that computer-readablestorage media is any available media that provides for thenon-transitory storage of data and that can be accessed by the serviceprovider network 102. In some examples, the operations performed by theservice provider network 102, and or any components included therein,may be supported by one or more server devices. Stated otherwise, someor all of the operations performed by the service provider network 102,and or any components included therein, may be performed by one or morecomputer devices operating in a cloud-based arrangement.

By way of example, and not limitation, computer-readable storage media206 can include volatile and non-volatile, removable and non-removablemedia implemented in any method or technology. Computer-readable storagemedia includes, but is not limited to, RAM, ROM, erasable programmableROM (“EPROM”), electrically-erasable programmable ROM (“EEPROM”), flashmemory or other solid-state memory technology, compact disc ROM(“CD-ROM”), digital versatile disk (“DVD”), high definition DVD(“HD-DVD”), BLU-RAY, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired information ina non-transitory fashion.

FIG. 3 illustrates a graphical user interface (GUI) 300 through which auser 105 of a service provider network 102 can define a workload 136 tolaunch on a VM instance 114.

The GUI 300 may be presented on a user device 108, and accessible via auser account 242 and a console 122. In some examples, the GUI 300 may bepart of the web-console wizard 212 that assists the user 105 inselecting an optimized or appropriate VM instance type 130 for a newworkload 136. The web-console wizard 212 may provide the users 105 withenough information for them to make a decision regarding a VM instancetype 130 that is appropriate for their workload 136.

The GUI 300 may include a name option 302 through which the user 105 mayprovide tagging information in the form of a name of the workload, orother unique identifier for the workload 130. In this case, the nameoption 302 indicates that the name is “My Workload,” which may be anyterminology used by the user to identify their workload and may be usedby the optimization component 126 to label resource groups associatedwith the workload. The GUI may further include a description portion304, such as a text field in which the user 105 may provide a briefdescription of the workload 130 and its intended purpose. Again, thistext may be provided to assist the user with recognizing this particularworkload among a group of their workloads, and/or may be utilized by theoptimization component 126 to map the workload data 120 to a predefinedworkload category 220, and thus a set of VM instance IDs 226 torecommend.

The GUI 300 may further include a workload-type field 306, which mayinclude drop-down selections of predefined answers that help theoptimization component 126 select an appropriate workload category 220.In this case, the selected predefined answer in the workload-type field306 is “Web Server,” which narrows the workload 130 to a web server. TheGUI 300 may additionally include one or more workload questions 308, andfields for answers, that help narrow-down the workload 130 of the user105. In this example, the questions may prompt the user 105 forinformation they may know, without requiring that the user 105 hastechnical or computing-resource knowledge. For example, the workloadsquestions 308 can include asking how many visitors are expected in aday, what length of time is acceptable for a webpage to load, and soforth. With these types of questions, unsophisticated users 105 canprovide valuable information in terms that make sense to them, ratherthan having them sort through VM instance types 130 with more complexterminology. For example, these types of workload questions 308 mayreplace questions including terminology such as “what kind of networkbandwidth will your workload need,” “what kind of storage requirementsdo you need to support your webpage,” or “what kind of latencyrequirements do you have for your webpage?”. However, the answers tothese types of questions may map the workload data 120 to appropriateworkload categories 220 and/or VM instance type 130 sizes (e.g., amountof allocated computing resources).

Further, the GUI 300 may include an environment option 310 where theuser 105 can indicate whether the workload 130 is production, orpre-production, as well as a list of account IDs 312 over which theworkload 130 spans (e.g., existing user accounts 242 the workload 130 isusable and/or configurable by). Once the user 105 has finished with thestep-by-step process provided by the web-console wizard 212, the user105 may select the define workload option 314 and generate the workloaddata 120. The workload data 120 may then be provided to the optimizationservice 106 via the user portal(s) 122, such as the console.

It should be understood that the GUI 300 is merely illustrative and anytype of user interface, or combination of user interfaces, may beutilized to prompt a user 105 for information that describes or definestheir workload 130. Additionally, any type of input mechanism may beused to receive input data (e.g., workload data 120) that can be used todefine a workload 130 in addition to text-input fields or drop-downselections.

FIG. 4. illustrates a system-architecture diagram 400 of a serviceprovider network 102 that utilizes anonymized historical-utilizationdata from VM instances and workloads across user accounts to generateworkload categories and resource-utilization models. According to theexamples described herein, the anonymized historical-utilization data412 is collected from workloads that are hosted on behalf of useraccounts 242 that have “opted-in” to allow the service provider network102 to collect the data. For instance, the opted-in accounts 242(1)-(N)may have all expressly allowed or opted-in to give permission to theservice provider network 102 to collect utilization data from theirworkloads to help improve the optimization service 106 described herein.Additionally, the utilization data collected may be anonymized togenerate anonymized historical-utilization data 412 that does notindicate from which opted-in account 242 the data is associated with. Inthis way, not only are the opted-in accounts 242 aware that theirutilization data is being harnessed, but the utilization data isanonymized using various techniques to generate the anonymizedhistorical-utilization data 412 that prevents the opted-in accounts 242from being identified based on the anonymized historical-utilizationdata 412, thereby protecting the privacy of opted-in accounts 242. Thus,not only do the accounts opt-in, but their identities are protected byusing anonymized historical-utilization data 412.

The computing-resource network 110 may include a plurality of computingdevices 402 interconnected by various networks. In some examples, thecomputing devices 402 may be positioned in data centers located acrossdifferent geographic regions (e.g., servers in datacenters) and providecomputing resources that are allocated amongst different VM instances404(1)-(N) (where “N” is any integer greater than 2 as described in thisapplication). Many different users may have created user accounts242(1)-(N) and requested that the service provider network 102 provisionand/or deploy various VM instances 404(1)-(N) on the computing resources402 to support different types of workloads 406(1)-(N). For example,each user account 242 may have at least one workload 406 supported on atleast one VM instance 404 per workload 406. The workloads 406 maycomprise a wide variety of workloads 406 as described above, and may beprovisioned on a wide variety of VM instance types 130. Accordingly, theservice provider network 102 has large amounts of workloads 406, hostedor supported on a wide variety of VM instances 404 of different VMinstance types 130, and supporting a wide variety of workloads 406 withdifferent resource-consumption characteristics.

The service provider network 102 may determine to utilize anonymizedhistorical-utilization data 412 from the VM instances 404 and theworkloads 406(1)-(N) to generate the workload categories 220 andresource-utilization models 224, and/or assign VM instance identifiers226 to workload categories 220 for which the corresponding VM instancetypes 130 are optimized. In such examples, the service provider network102 may collect the anonymized historical-utilization data 412 in a datastore 208, such as one or more repositories or storage locations. Todetermine or generate the workload categories, the service providernetwork 102 may utilize the clustering component 218 to cluster the widevariety of workloads 406 based on the anonymized historical-utilizationdata 412. More specifically, the clustering component 218 may analyzethe anonymized historical-utilization data 412 to cluster the differenttypes of workloads 406 into workload categories 220 based onsimilarities between the anonymized historical-utilization data 412 forthe different workloads 406.

The clustering component 218 may generate or determine the workloadcategories 220 using various clustering or classification techniques.The clustering techniques performed by the clustering component 218 maybe unsupervised clustering techniques, supervised clustering techniques,partially supervised clustering techniques, and/or any combinationthereof. For example, the clustering component 218 may cluster in timeseries where individual time series of the anonymizedhistorical-utilization data 412 is grouped based on similar time seriesinto a same cluster. As a specific example, game-hosting servers mayscale more during the day, or certain hours of the day, such as eveningswhen players are no longer working. More broadly, workloads 406 thathave spikes and lulls in particular types of computing resources (e.g.,CPU, memory, disk, network bandwidth, GPU, etc.) may be clustered intothe same workload categories 220. The clustering component 218 maydetermine a number of clusters (e.g., based on the number of workloadtypes), and utilize a clustering method, such as k-means clustering, tocluster types of the workloads 406 into the workload categories 220until a sufficient amount of the workloads 406 have been assigned to aworkload category 220 such that the workload categories aresubstantially representative of the different types of workloads 406supported by the service provider network 102. The clustering component218 may then be utilized to assign names to the workload categories 220based on, for example, tagging data associated with the workloads 406.For example, a workload category 220 representing workloads 406 thatoften have a name assigned to them via the GUI 300 that includes“website” may be called a “website” cluster. In some examples, theworkload category 220 may be assigned a name based on the anonymizedhistorical-utilization data 412 for the represented workloads 406. Forinstance, a workload category 220 that represents workloads 406 withheavy CPU consumption may be named “compute-heavy.” In this way, theclustering component 218 may create, generate, or otherwise defineworkload categories 220 that are representative of different types ofworkloads 406 across the computing-resource network 110.

The ML component 222 may be configured to generate theresource-utilization models 224 for each of the workload categories 220.The ML component 222 may perform various techniques, and utilize variousML algorithms, to train one or more resource-utilization models 224 thatrepresent resource-utilization characteristics representative of theworkloads 406 in each workload category 220. For instance, when theworkload categories 220 have been generated, the ML component 222 mayanalyze the anonymized historical-utilization data 412 for the workloads406 in each workload category 220 and determine a resource-utilizationmodel 224 for that workload category 220 that generally represents theincluded workloads 406.

The ML component 222 may utilize various machine learning techniques oralgorithms to generate the resource-utilization models 224. As aspecific example, the ML component 222 may represent or normalize thedimensions of compute for the anonymized historical-utilization data 412of each of the workloads 406 and create feature data representing theanonymized historical-utilization data 412. Specifically, the MLcomponent 222 may generate feature vectors that represent the anonymizedhistorical-utilization data 412 across the dimensions of compute (e.g.,CPU, GPU, memory, disk, and network throughput) for each workload 406.The ML component 222 may then utilize the feature data of the anonymizedhistorical-utilization data 412 as input into an ML algorithm, such as aneural network, regression algorithms, classification algorithms, and/orany other ML algorithm, and train the resource-utilization models 224.In this way, the ML component 222 may generate resource-utilizationmodels 224 for each workload category 220 using anonymizedhistorical-utilization data 412 for workloads 406 hosted by VM instances404 across the computing-resource network 110. The resource-utilizationmodels 224 may be representative of resource-utilization data 138 forthe workloads 406 that are included in the workload categories 220. Inthis way, when a new workload 136 needs to be categorized for purposesof identifying optimized VM instance types 130, the resource-utilizationdata 138 for the new workload 136 may be mapped to theresource-utilization model 224 that is “closest” or “most near” thefingerprint of the resource-utilization data 138 for the new workload136. In some examples, the ML component 222 may utilize other types ofdata to train the resource-utilization models 224, such asinfrastructure supporting the workloads 406-410, health status checksfor the workloads 406-410, and/or other types of data indicatingperformance for the workloads 406-410 on the different VM instances404(1)-404(N). In some examples, the infrastructure supporting theworkload may include information regarding network topology, networksecurity groups (e.g., protocols and port ranges), network trafficpatterns, presence and configuration of load balancers, and scalingtriggers for auto scaling groups to learn more about workloads 406-410.Some, or all, of this infrastructure information associated withinfrastructure supporting the workloads 406-410 may be utilized to trainthe resource-utilization models 224.

The simulation component 234 may then analyze various data to determinethe VM instance identifiers 226 that are to be assigned to the workloadcategories 220 as being optimized or computationally biased to supportthe workloads 406 in the categories 220. For instance, the simulationcomponent 234 may simulate various workloads on VM instances 404 inorder to collect performance data indicating how well the simulatedworkloads performed on the various VM instances 404. In some examples,the simulation component 234 may simulate consumption of computingresources by mimicking utilization similar to that of theresource-utilization models 224 assigned to each workload category 220on the VM instances 404. The simulation component 234 may then collectsimulation data that represents performance (e.g., data throughputcompared to computing resources allocated to the VM instance 404supporting a simulated workload), and determine which VM instances 404perform well, or are optimized, for the different resource-utilizationmodels 224. The simulation component 234 may then assign VM instancetypes 130 to workload categories 220 based on how well the VM instancetypes 130 perform when supporting the simulated workloads 406 for thosecategories 220. The simulation component 234 may assign VM instanceidentifiers 226 to workload categories based on the VM instance types130 determined to be optimized for the workload categories 220. Thesimulation component 234 may further provide result data 414 back to theML component 222 to utilize to further train the resource-utilizationmodels 224. The result data 414 may include the simulation results forthe simulation component 234 simulating the different workloads on thedifferent VM instance types 130 to determine resource-utilization datafor simulated workloads.

Thus, resource-utilization data 138 for workloads 136 may be mapped to,or matched to, workload categories 220 that are associated withresource-utilization models 224 that have the most similarresource-utilization characteristics. The workloads 136 may then becategorized as belonging to the workload category 220 that is associatedwith the most similar resource-utilization model(s) 224. Once assignedto a workload category 220, the optimization component 126 maydetermine, based on the associated VM instance identifiers 226, which ofthe VM instance types 130 are optimized to support the workload 136. Insome examples, the ML component 222 may train the resource-utilizationmodels 224 using only the anonymized historical-utilization data 412,only the result data 414 from the simulation component 234, acombination thereof, and/or any other data.

FIG. 5 illustrates an example architecture 500 including a graphicaluser interface 502 through which a user 105 of a service providernetwork 102 can review recommendations regarding VM instance types 130that are optimized to support their workload 130. In some examples, theGUI 502 may present data received as part of the recommendation data 132from the service provider network 102. Additionally, the GUI 502 may bepresented on the user device 108, and accessible via a user account 242and the console 122. In some examples, the GUI 502 may be part of theweb-console wizard 212 that assists the user 105 in selecting anoptimized or appropriate VM instance type 130 for launching a newworkload 136.

The GUI 502 may comprise options through which a user 105 can select orchoose a VM instance type 130. The GUI 502 may list different VMinstances types 130 that have been determined by the optimizationservice 106 as being optimized for the workload 130 associated with theuser account 242 through which the console 122 is accessed. The GUI 502may allow the user 102 to select one of the VM instance types 130 tolaunch their workload 130 in an automated fashion.

As shown, the GUI 502 may present instance type 504, suitability 506,and explanations 508 for the recommended VM instance types 130. In theillustrated example, a first VM instance 510 may be storage optimized,have a suitability 506 of 4.5 out of 5 stars, and have an explanation508 indicating that the VM instance type 510 delivers additional storagewith sufficient compute for the workload 130. Similarly, a second VMinstance 512 may be network-bandwidth optimized, have a suitability 506of 4 out of 5 stars, and have an explanation 508 indicating that the VMinstance type 512 delivers additional network bandwidth for additionaltraffic with sufficient memory for the workload 130. Finally, a third VMinstance 514 may be general purpose, have a suitability 506 of 3 out of5 stars, and have an explanation 508 indicating that the VM instancetype 514 delivers cost saving with sufficient resources for the workload130.

Using this recommendation data 132, the user 105 can make a moreinformed decision as to what VM instance type 130 to utilize to supporttheir workload 130, check a box next to the VM instance type 130 theydesire, and further provide input into a select instance type control516. Upon selecting the instance type, selection data 518 may be sentfrom the user device 108, over the network(s) 118, to the serviceprovider network 102 to indicate that the user 105 is requesting to havetheir workload 130 launched or supported by the first VM instance type510.

It should be understood that the GUI 502 is merely illustrative, and anytype of user interface, or combination of user interfaces, may beutilized to receive input data indicating a selection of a recommendedVM instance type 130. Additionally, any type of input mechanism may beused to receive input data (e.g., selection data 518) that can be usedto select a VM instance type 130 than that described and illustrated.

FIGS. 6A, 6B, 7, 8, 11-13, 15, 16, 18, 19, and 24-26 illustrate flowdiagrams of example methods 600, 700, 800, 1100, 1200, 1300, 1500, 1600,1800, 1900, 2400, 2500, and 2600 that illustrate aspects of thefunctions performed at least partly by the service provider network 102as described in this disclosure. The logical operations described hereinwith respect to FIGS. 6A, 6B, 7, 8, 11-13, 15, 16, 18, 19, and 24-26 maybe implemented (1) as a sequence of computer-implemented acts or programmodules running on a computing system and/or (2) as interconnectedmachine logic circuits or circuit modules within the computing system.

The implementation of the various components described herein is amatter of choice dependent on the performance and other requirements ofthe computing system. Accordingly, the logical operations describedherein are referred to variously as operations, structural devices,acts, or modules. These operations, structural devices, acts, andmodules can be implemented in software, in firmware, in special purposedigital logic, and any combination thereof. It should also beappreciated that more or fewer operations might be performed than shownin the FIGS. 6A, 6B, 7, 8, 11-13, 15, 16, 18, 19, and 24-26 anddescribed herein. These operations can also be performed in parallel, orin a different order than those described herein. Some or all of theseoperations can also be performed by components other than thosespecifically identified. Although the techniques described in thisdisclosure is with reference to specific components, in other examples,the techniques may be implemented by less components, more components,different components, or any configuration of components.

FIGS. 6A and 6B collectively illustrate a flow diagram 600 of an examplemethod for a service provider network 102 to receive a definition of aworkload 136 from a user account 242 through one or more userinterfaces, mapping the workload 136 to a predefined workload category220, and provide the user account 242 with a recommendation 132 as to aVM instance type 130 to support their workload 136. As described herein,a virtual computing resource may comprise one or more of a VM instance114, a virtual container, a program, and/or any other virtualrepresentation.

In some examples, the techniques of method 600 are performed using asystem that includes a computing resource network 110 of a serviceprovider network 102 that is managed by a service provider 104. Thecomputing resource network 110 may be configured to support at least afirst virtual machine (VM) instance type 130 configured to utilize afirst combination of types of the computing resources to supportworkloads 136, and a second virtual computing resource type 130configured to utilize a second combination of the types of the computingresources to support workloads 136.

In some examples, the techniques of method 600 may be performed by aoptimization service 106 that includes one or more processors 202 andone or more computer-readable media 206 storing computer-executableinstructions that, when executed by the one or more processors, causethe one or more processors to perform the operations of method 600.

At 602, the optimization service 106 may receive, via a user account 242registered with the service provider network 102, a first request tosupport a workload 136 on behalf of the user account 242 using thecomputing resources of the computing-resource network 110. For example,the user account 242 may be utilized by a user 105, via their userdevice 108, to interact with the user portal 122 to request that aworkload 136 be hosted in the computing-resource network 110.

At 604, the optimization service 106 may receive, via the user account242, a second request for a recommendation of a virtual computingresource type 130 that is optimized to support the workload 136. Forinstance, the user 105 may indicate via their user account 242 that theywould like to opt-in for use of the optimization service 106 to providethem with a recommendation of a virtual computing resource type 130 thatis optimized to support their workload 136.

At 606, the optimization service 106 may provide the user account 242with access to one or more user interfaces 214 configured to receiveinput data describing the workload 136. For example, the web-consolewizard 212 may present one or more GUIs 300 that help the user 105 inputdata that defines the workload 136 using language or input mechanismsthat comes natural to the user 105.

At 608, the optimization service 106 may receive, via the one or moreuser interfaces 214, input data that indicates resource-utilizationcharacteristics associated with supporting the workload 136. Forexample, the user 105 may input, via the GUI(s) 300, workload data 120that indicates resource-utilization characteristics of the workload 136.

At 610, the optimization service 106 may map, based on theresource-utilization characteristics, the workload 136 to a workloadcategory 220 from a group of predefined workload categories 220. In someexamples, the workload category 220 represents workloads supported by athird combination of the types of the computing resources. Statedotherwise, the workload category 220 may be associated with aresource-utilization model 224 that represents amounts and/orcombinations of types of the computing resources that are utilized tosupport the representative workloads 136 of that workload category 220.

At 612, the optimization service 106 may determine that the firstvirtual computing resource type 130 is optimized to support the workload136 based on the third combination of the types of the computingresources corresponding to the first combination of the types of thecomputing resources. Stated otherwise, the optimization service 106 maydetermine that the amount and/or combination of types of computingresources indicated by the workload data 120 for the workload 136 maycorrespond to a virtual computing resource 130 that is optimized forthose resource-utilization characteristics.

At 614, the optimization service 106 may generate recommendation data132 including an indication that that the first virtual computingresource type 130 is optimized to support the workload 136 requested bythe user account 242. For example, the optimization service 106 maygenerate recommendation data 132 that includes a virtual computingresource type listing 228 of one or more of the virtual computingresource identifiers 226 for the workload category 220.

At 616, the optimization service 106 may provide the user account 242with access to the recommendation 132. For example, the user 105 may loginto their user account 242 and access the user portal 122 (e.g.,console) to view the GUI 502 that includes a listing of optimizedvirtual computing resource types 130.

In some examples, the recommendation data 122 may further includesuitability data 230 indicating a measure of suitability for the firstvirtual computing resource type 130 to support the workload 136requested by the user account 232, and text data including a textualexplanation 508 regarding the suitability of the first virtual computingresource type 130 for supporting the workload 136.

In some examples, the types of computing resources may include at leasttwo of a central processing unit (CPU) resource type, a memory resourcetype, a storage resource type, or a network availability resource type.In such examples, the first combination of the types of the computingresources utilized by the first virtual computing resource typecomprises a first amount of a first type of the types of the computingresources, and a second amount of a second type of the types of thecomputing resources. Further, the second combination of the types of thecomputing resources utilized by the second virtual computing resourcetype comprises a third amount of the first type of the types of thecomputing resources, and a fourth amount of the second type of the typesof the computing resources.

In some instances, the input data comprises first input data, and theoptimization service 106 comprises further computer-executableinstructions that, when executed by the one or more processors, causethe one or more processors to receive, from the user account 242, secondinput data indicating a selection of the first virtual computingresource type 130 to support the workload 136, and cause a virtualcomputing resource 114 corresponding to the first virtual computingresource type 130 to be provisioned in the computing resource network110 to support the workload 136 on behalf of the user account 242. Forexample, the user 105 may utilize their user account 242 to access GUI502 and select an instance type 504 and utilizing the select instancetype control 516.

FIG. 7 illustrates a flow diagram 700 of an example method for a serviceprovider network 102 to receive a resource-utilization characteristic138 for a workload 136, and provide a recommendation 132 to a useraccount 242 regarding a virtual computing resource type 130 that is tobe used to launch the workload 136.

At 702, the optimization service 106 may receive, via a user account 242associated with the service provider network 102, a request to launch aworkload 136 using at least one virtual machine (VM) instance 114 thatis supported by computing resources associated with the service providernetwork. In some examples, the computing resources support at least afirst virtual computing resource type 130 that is allocated a firstamount of the computing resources, and a second virtual computingresource type 130 that is allocated a second amount of the computingresources.

At 704, the optimization service 106 may receive utilization data 138indicating a resource-utilization characteristic of the workload 136during execution. The resource-utilization characteristic may indicateat least one of an amount of the computing resources consumed by theworkload 126 or a type of the computing resources consumed by theworkload 136.

At 706, the optimization service 106 may determine, based at least inpart on the resource-utilization characteristic 138, that the workload136 corresponds to a workload category 220 of a group of predefinedworkload categories 220. For example, the optimization component 126 maydetermine that the resource-utilization characteristic (e.g., CPU, GPU,memory, disk, network throughput, etc.) may at least partly correspondor match to a resource-utilization model 224 for the workload category220.

At 708, the optimization service 106 may determine that the workloadcategory 220 is associated with workloads 136 that consume a thirdamount of the computing resources that at least partly corresponds tothe first amount of the computing resources. For example, theoptimization service 106 may determine that the workload category 220 isassociated with virtual computing resource identifiers 226 thatrepresents virtual computing resource types 130 that consume thirdamounts of the computing resources that correspond the first amount ofcomputing resources consumed by the workload 136.

At 710, the optimization service 106 may provide, to the user account242, recommendation data 132 including a recommendation that the firstvirtual computing resource type 130 be used to launch the workload 136.For instance, the user device 108 may receive the recommendation data132, and present the GUI 502 that indicates that the first virtualcomputing resource type 130 be used to launch the workload 136.

In some instances, the user 105 may have been hosting or supportingtheir workload 136 using computing resources of a remote computingresource network. In such examples, the optimization service 106 mayreceive, from the user account 242, an indication the workload 136 isbeing migrated from being supported by second computing resourcesincluded in a computing resource network remote from the serviceprovider network 102. Further, the optimization service 106 may providethe user account 242 with an interface configured to receive resourceconsumption data associated with consumption of the second computingresources by the workload 136. For example, the user 105 may providehistorical computing resources for their workload 136 being hosted onremote computing resources. In such examples, receiving the utilizationdata 138 includes receiving, via the interface, the resource consumptiondata.

In some examples, the method 700 may further include receiving, from theuser account 242, input data indicating a selection of the first virtualcomputing resource type 130 to be used to launch the workload 136. Forexample, the user 105 may provide input to the select instance typecontrol 516 to select the first virtual computing resource type 130.Further, the optimization service 106 may cause the workload 136 to belaunched at least partly using a virtual computing resource 114corresponding to the first virtual computing resource type 130 that issupported by the computing resources. For example, the optimizationservice 106 may send an instruction to the compute-management service134 to launch the workload 136.

FIG. 8 illustrates a flow diagram of an example method 800 for a serviceprovider network 102 to receive input via one or more user interfacesthat indicates a resource-utilization characteristic associated with aworkload, determines a virtual computing resource type based on theresource-utilization characteristic, and provides a recommendation to auser account for the virtual computing resource type to support theworkload.

At 802, the service provider network 102 may receive, via a useraccount, a request to launch a workload using at least one virtualmachine (VM) instance that is supported by computing resourcesassociated with the service provider network. The computing resourcessupport at least a first virtual computing resource type that isallocated a first amount of the computing resources, and a secondvirtual computing resource type that is allocated a second amount of thecomputing resources.

At 804, the service provider network 102 may provide the user accountwith access to one or more user interfaces configured to receive inputdata associated with the workload. At 806, the service provider network102 receive, at least partly via the one or more user interfaces, inputdata that indicates a resource-utilization characteristic associatedwith the workload.

At 808, the service provider network 102 may determine, based at leastin part on the resource-utilization characteristic, that the firstvirtual computing resource type is optimized to at least one of launchor execute the workload. Further, at 810, the service provider network102 may provide, to the user account, recommendation data including arecommendation that the first virtual computing resource type be used toat least one of launch or execute the workload.

FIG. 9 illustrates a system-architecture diagram 900 of a serviceprovider network 102 that simulates workloads on new VM instance typesto determine performance of the new VM instance type, and testsworkloads on VM instances before migrating fleets of workloads onto newVM instance types.

To provide additional functionality and a wider variety of VM instancetypes 128, the service provider 104 may continue to introduce new VMinstance types 902 for use by the users 105. To determine whether thenew VM instance type 902 is optimized for various workloads 136 and/orworkload categories 220 without having to actually have users 105 be“guinea pigs” and test the new VM instance type 902, the optimizationservice 106 may utilize the simulation component 234 to determine thecomputational bias(es) of the new VM instance type 902.

The simulation component 234 may utilize one or more simulation VMinstances 904 on one or more computing devices 112 to simulate thedifferent workloads 136 using simulation workloads 906. The simulationcomponent 234 may provision, deploy, and monitor the simulation VMinstance 904 that corresponds to the new VM instance type 902, andsimulate various workloads using a simulation workload 906. In someexamples, the simulation workload 906 may be a simulator program that isconfigured to consume designated amounts of computing resources suchthat the simulation component 234 can mimic actual workloads 136 andworkload categories 220. The simulation component 234 can then receivethe simulation data 908 in order to determine what workloads 136 areoptimized for the new VM instance type 902 (e.g., throughput compared toallocated computing resources). Thus, the simulation component 234 maysimulate the consumption of different workloads 136 (e.g., simulationworkload 906) using a simulation program on the new VM instance types902, and determine performance metrics that indicate throughput of datafor amounts of computing resources input into the new VM instance type902.

The simulation component 234 may comprise at least one process that isconfigurable to consume different amounts of computing resources of thecomputing devices 112. For instance, the simulation component 234 maydrive compute utilization that is equivalent to how different workloads906 look or consume. The simulation component 234 may scale theconsumption of the different compute dimensions by scaling up or downthe amount of computing resources consumed. For example, the simulationworkload 906 may read or write an amount of data to disk, consume CPUand memory using processes, send data over networks, and so forth tosimulate target consumption to test various workloads 136.

The simulation component 234 may then determine which workloads 136and/or workload categories 220 for which the new VM instance type 902 isoptimized, and then assign VM instance identifiers 226 corresponding tothe new VM instance type 902 to the workload categories 220. In thisway, user accounts 242 with workloads 136 that may be optimized on thenew VM instance type 902 may be provided with recommendation data 132indicating that migrating their workloads 136 to the new VM instancetype 902 may be advantageous.

In some examples, the optimization service 106 may test workloads 136 onVM instances 114 before migrating fleets of workloads 136 onto new VMinstance types 902 and/or existing VM instance types 130. For instance,the testing component 236 may utilize test VM instances 910 to supporttest workloads 912 that correspond to workloads 136 of users 105. Theoptimization service 106 may allocate computing devices 112 to supporttest VM instances 910 and the testing component 236 may use these testVM instances 910 and test workloads 912 to determine whether a workload136 actually performs well, or is further optimized, when placed on anew VM instance type 902 and/or an existing VM instance type 130 ascompared to a current VM instance 114. For example, the testingcomponent 236 may “spin up” or provision a test VM instance 910corresponding to a VM instance type 130 and/or new VM instance type 902that the optimization component 126 has determined is optimized for aworkload 136. Prior to recommending the VM instance type 130/912 to auser 105, the testing component 236 may first test the test workload 912on the test VM instance 910 and receive health data 914 indicating howwell the test workload 912 is performing. Based on the health data 914,the testing component 236 can provide insight to the optimizationcomponent 126 regarding whether or not the new VM instance type 130/902is in fact optimized compared to the current VM instance type 130 forthe workload 136 (e.g., determine throughput compared to allocatedcomputing resources). Based on the results of the simulation component234 and/or the testing component 236, the recommendation engine 124 maygenerate and send recommendation data 916 to the users 105 that haveworkloads 136 associated with their user accounts 242 that may be moreoptimized on a different VM instance type 130/902. Generally, thetesting component 236 may utilize the test workload 912 in anenvironment where it is allowed to fail, such as a workload 136environment where the workloads 136 are redundant (e.g., batch process).

FIG. 10 illustrates an example architecture 1000 including a graphicaluser interface (GUI) 1002 through which a user 105 of a service providernetwork 102 can review recommendations to migrate their workload 136 toa new VM instance type 130 that is optimized to support their workload136. The GUI 1002 may be presented on the user device 108, andaccessible via a user account 242 and the console 122. In some examples,the GUI 1002 may be part of the web-console wizard 212 that assists theuser 105 in selecting an optimized or appropriate VM instance type 130for resizing an existing workload 136.

The GUI 1002 can include a textual explanation 1004 that the user 105should migrate their workload 136 due to lower resource utilizationrecently, and that the smaller VM instance type 130 will not sacrificeperformance. The GUI 1002 may further include an indication of thecurrent instance type 1006, and an indication of the recommended newinstance type 1008. Further, the GUI 1002 may recommend that the user105 consider installing a monitoring agent on their VM instances 114 inorder to get more precise recommendations, as described in more detailin FIGS. 14-16. Additionally, the GUI 1002 can let the user 105 knowthat their VM instance 114 is running, and that changing the VM instancetypes 130 will restart the VM instance 114.

Using this recommendation data 916 presented in the GUI 1002, the user105 can make a more informed decision as to what VM instance type 130 toutilize to support their workload 130, and further provide input into anapply control 1014. Upon selecting the apply control 1014, selectiondata 1016 may be sent from the user device 108, over the network(s) 118,to the service provider network 102 to indicate that the user 105 isrequesting to have their workload 130 migrated to the new VM instancetype 130.

It should be understood that the GUI 1002 is merely illustrative, andany type of user interface, or combination of user interfaces, may beutilized to receive input data indicating a selection of a recommendedVM instance type 130. Additionally, any type of input mechanism may beused to receive input data (e.g., selection data 1016) that can be usedto migrate to a new VM instance type 130 than that described andillustrated.

FIG. 11 illustrates a flow diagram of an example method 1100 fordetermining that a new virtual computing resource type is more optimizedto support a workload than a current virtual computing resource type,recommending the new virtual computing resource type to a user accountassociated with the workload, and migrating the workload to the newvirtual computing resource type. As described herein, a virtualcomputing resource may comprise one or more of a virtual computingresource 114, a virtual container, a program, and/or any other virtualrepresentation.

At 1102, the service provider network 102 may provision a first virtualmachine (VM) instance on computing resources associated with the serviceprovider network that is managed by a service provider, wherein thefirst virtual computing resource is of a first virtual computingresource type that is allocated a first amount of the computingresources for utilization.

At 1104, the service provider network 102 may deploy a workload to besupported by the first virtual computing resource on behalf of a useraccount registered with the service provider network, wherein theworkload is associated with a resource-utilization characteristicindicating utilization of the computing resources by the workload.

At 1106, the service provider network 102 may identify a second virtualcomputing resource type that has been made available for use to supportworkloads on behalf of user accounts registered with the serviceprovider network, wherein the second virtual computing resource type isallocated a second amount of the computing resources for utilization.

At 1108, the service provider network 102 may determine, based on theresource-utilization characteristic, that the second virtual computingresource type is more optimized to support the workload than the firstvirtual computing resource type.

At 1110, the service provider network 102 may provide the user accountwith recommendation data including a recommendation to migrate theworkload from being supported by the first virtual computing resourcetype to be supported by the second virtual computing resource type.

At 1112, the service provider network 102 may receive input dataindicating a request from the user account to migrate the workload frombeing supported by the first virtual computing resource type to besupported by the second virtual computing resource type.

At 1114 service provider network 102, the migrate the workload to besupported by a second virtual computing resource on behalf of the useraccount, wherein the second virtual computing resource is of the secondvirtual computing resource type.

FIG. 12 illustrates a flow diagram of an example method 1200 fordetermining that the resource utilization of a workload changed,identifying a new virtual computing resource type that is more optimizedto host the workload, and providing a user account with a recommendingto migrate the workload to the new virtual computing resource type.

At 1202, the service provider network 102 may host a workload on behalfof a user account at least partly using a first virtual machine (VM)instance that is provisioned on first computing resources of a serviceprovider network, wherein the first virtual computing resource is of afirst virtual computing resource type that is allocated the firstcomputing resources for utilization.

At 1204, the service provider network 102 may determine that theworkload changed from utilizing a first amount of the first computingresources to utilizing a second amount of the first computing resources.

At 1206, the service provider network 102 may identify, based at leastin part on the second amount of the first computing resources, a secondvirtual computing resource type that is more optimized to host theworkload than the first virtual computing resource type, wherein thesecond virtual computing resource type is allocated second computingresources for utilization. In some examples, the service providernetwork 102 may determine that a difference between the first amount ofthe computing resources and the second amount of the computing resourcesis greater than a threshold difference, and identify the second virtualcomputing resource type is performed based at least in part on thedifference being greater than the threshold difference. For instance,the service provider network 102 may determine that the optimization isgreat enough or “worth it” to migrate the workload.

At 1208, the service provider network 102 may provide recommendationdata to the user account including a recommendation to migrate theworkload from being hosted by the first virtual computing resource to asecond virtual computing resource that is of the second virtualcomputing resource type.

In some examples, the method of 1200 may further comprise collecting,from the first virtual computing resource at a first time, a firstutilization value indicative of the first amount of the first computingresources, determining that a period of time has elapsed from the firsttime, wherein the period of time is associated with a frequency at whichthe user account modifies the workload, and collecting, from the firstvirtual computing resource at a second time, a second utilization valueindicative of the second amount of the first computing resources. Statedotherwise, the service provider network 102 may collect the utilizationvalues according to frequency or period of time.

In some examples, the method of 1200 may determine that the secondvirtual computing resource is associated with an optimization value thatis greater by a threshold amount than an optimization value of the firstvirtual computing resource.

FIG. 13 illustrates a flow diagram of an example method 1300 fordetermining that a new virtual computing resource type is more optimizedto support a workload than a current virtual computing resource type,and recommending the new virtual computing resource type to a useraccount associated with the workload.

At 1302, service provider network 102, the service provider network 102host a workload on behalf of a user account at least partly using afirst virtual machine (VM) instance that is provisioned on firstcomputing resources of a service provider network, wherein the firstvirtual computing resource is of a first virtual computing resource typethat is allocated the first computing resources for utilization.

At 1304, the service provider network 102 may identify a second virtualcomputing resource type that has been made available for use to hostworkloads on behalf of user accounts associated with the serviceprovider network, wherein the second virtual computing resource type isallocated second computing resources for utilization.

At 1306, the service provider network 102 may receive aresource-utilization characteristic indicating utilization of the firstcomputing resources by the workload. At 1308, the service providernetwork 102 may determine, based on the resource-utilizationcharacteristic, that the second virtual computing resource type is moreoptimized to host the workload than the first virtual computing resourcetype.

At 1310, the service provider network 102 may provide the user accountwith recommendation data including a recommendation to migrate theworkload from being hosted by the first virtual computing resource to behosted by a second virtual computing resource that is of the secondvirtual computing resource type.

FIG. 14 illustrates a system-architecture diagram 1400 of an exampleenvironment in which an optimization service 106 of a service providernetwork 102 receives configuration data from an agent executing on a VMinstance that supports a workload, and recommends that modifications bemade to a configuration parameter of the workload or operating system.

In some examples, a user 105 may access a dashboard 1402 using theiruser account 242 and indicate that they would like to install amonitoring agent for more precise recommendations regarding theirworkloads 136 beyond recommending a VM instance type 130 on which tohost the workloads 136. The user devices 108 may transmit the requestdata 1404 to the optimization service 106 indicating the request formore detailed recommendations. In such examples, the configurationcomponent 1406 may, at 1408, deploy an agent 1410 onto the VM instance114.

The optimization component 126 may include a configuration component1406 that determines modifications to make to configuration parametersof an application stack of a workload 136, or an operating system of aVM instance 114, to optimize performance of the workload 136 and/or VMinstance 114. To collect the data required to determine themodifications to make to the configuration parameters, the configurationcomponent 1406 may deploy or install the agent 1410 to the VM instance114 that is configured to collect configuration data indicatingparameters of the operating system 1412 on the VM instance 114, and/orthe application stack 1414 of the workload 134 on the VM instance 114.As illustrated, the agent 1410 may perform one or more steps to collectconfiguration data 1416, and provide configuration data 1418 to the datastore 208 associated with the optimization service 106. The data store208 may store the agent-provided configuration data 1420 (e.g.,configuration data 1418), as well as inferred configuration data 1422.

The agent 1410 may collect configuration data 1418 such as memoryutilization by processes running inside the VM instance 114,configurations for processes running on the VM instance 114, whatversions of the software and/or operating system 1412, how much memoryis configured for the application stack 1414, how many threads arerunning, the connection timeouts for the application stack 1414, and/orother types of configuration data 1418. Additionally, various parametersor configuration data may be obtained for the operating system 1412,such as what version of the operating system 1412, the CPU usage by theOS 1412, how many concurrent file handles are allowed, what the networkstack configure is (e.g., buffer size), and/or other parameters.Additional types of configuration data 1418 may include parameters for adisk subsystem, such as if customers are using RAID, what their backingis (e.g., EBS), and so forth.

In some examples, the configuration component 1406 may collect theinferred configuration data 1422 without the help of the agent 1410. Forinstance, the configuration component 1406 may observe security groupsfor the VM instance 114 to determine inbound traffic types and ports,identify auto-scaling groups for a fleet of VM instances 114 todetermine scaling policies, customer CPU utilization goals, determine amachine image used for the VM instance 114 which indicates the operatingsystem 1412 and version, VM instance 114 tagging data indicating usefulinformation about the VM instance 114, and/or other data.

The configuration component 1406 may determine modifications to make toa parameter for at least one of the operating system 1412 and/orapplication stack 1414 using the configuration data 1418 and/or inferredconfiguration data 1422. The configuration component 1406 may utilizerules-based analysis to generate parameter tuning suggestions forattributes like kernel, network stack, file system, memory management,application parameters, and so forth. The configuration component 1406may have predefined configuration parameters that are optimized for theoperating system 1412 and application stack 1414 for the different VMinstances 114 and/or workloads 134. In this way, the configurationcomponent 1406 may determine a modification to at least oneconfiguration parameter of at least one of the operating system 1412 orthe application stack 1414 based on differences between optimizedconfiguration parameters and the parameters indicated in theagent-provided configuration data 1420 and/or the inferred configurationdata 1422.

The recommendation engine 124 may generate recommendation data 1424 thatindicates the modification to the configuration parameter that optimizedthe at least one of the application stack 1414 or the operating system1412 and provide the recommendation data 1424 to the user device 108over the network(s) 118. The user 105 may determine whether they want toapply the modifications, and send a request back to the optimizationservice 106 to implement the modification to the configurationparameter. In some instances, the user 105 may opt-in to allow theconfiguration component 1406 to automatically modify configurationparameters to optimize the application stack 1414 and/or operatingsystem 1412. The configuration component 1406 may then make themodification to the configuration parameter if the user 105 hasindicated they would like to have the modification made to optimizeperformance of the application stack 1414 and/or the operating system1412 to support the workload 136.

FIG. 15 illustrates a flow diagram of an example method 1500 fordeploying a software agent to a virtual computing resource that ishosting a workload, receiving configuration data from the agent, andproviding a recommendation to a user account indicating a modificationto be made to a configuration parameter of an application stack of theworkload. As described herein, a virtual computing resource may compriseone or more of a VM instance 114, a virtual container, a program, and/orany other virtual representation.

At 1502, the service provider network 102 may host a workload using avirtual machine (VM) instance that is provisioned on hardware resourcesof the service provider network.

At 1504, the service provider network 102 may receive, via a useraccount associated with the workload, a request for a recommendation tooptimize an application stack of the workload.

At 1506, the service provider network 102 may deploy a software agent tothe virtual computing resource that is hosting the workload on behalf ofthe user account. At 1508, the service provider network 102 may receive,from the software agent, configuration data that specifies configurationparameters of the application stack of the workload. At 1510, theservice provider network 102 may determine a modification to aconfiguration parameter of the configuration parameters of theapplication stack to optimize the application stack of the workload. At1512, the service provider network 102 may provide, to the user account,recommendation data indicating the modification to the configurationparameter of the application stack to optimize the application stack.

In some examples, determining the modification of the configurationparameter to optimize the application stack of the workload may be basedat least in part on receiving utilization data indicating an amount ofthe hardware resources utilized to host the workload. For instance, theutilization data may indicate a workload category for the workload. Theresource-utilization models 224 may indicate optimized configurationparameters for at least one of the workload category or the workloaditself. The service provider network 104 may determine that theconfiguration parameter of the application stack is different than theoptimized configuration parameters for application stacks of theresource-utilization model, and determine the modification based on thedifference. The simulation component 234 may have been utilized todetermine, by simulating workloads 136 with different configurationparameters across difference instance types, the optimized configurationparameters for the different workloads 136 and/or workload types 130. Inthis way, modifications to the configuration parameters may bedetermined based on the optimized configuration parameters that helpoptimize performance, such as throughput, of the application stacks ofthe workloads 136 and/or operating systems of the VM instances 114.

FIG. 16 illustrates a flow diagram of an example method 1600 forreceiving configuration data that specifies a configuration parameter ofat least one of an application stack or an operating system, and eitherproviding a recommendation to a user account indicating a modificationto be made to a configuration parameter or automatically modifying theconfiguration parameter.

At 1602, the service provider network 102 may host a workload using avirtual machine (VM) instance that is provisioned on hardware resourcesof a service provider network. At 1604, the service provider network 102may receive configuration data that specifies a configuration parameterfor at least one of an application stack of the workload or an operatingsystem (OS) of the virtual computing resource.

At 1606, the service provider network 102 may determine a modificationto the configuration parameter that optimizes at least one of theapplication stack of the workload or the operating system of the virtualcomputing resource.

At 1608, the service provider network 102 may provide, to a user accountassociated with the workload, recommendation data indicating themodification to the configuration parameter that optimizes the at leastone of the application stack of the workload or the operating system ofthe virtual computing resource.

FIG. 17 illustrates a system-architecture diagram 1700 of an exampleenvironment in which an optimization service 106 of a service providernetwork 102 receives utilization data indicating resource consumption byworkloads on different computing devices, and maps the computing devicesto physical resource consumed to determine performance metrics for thecomputing devices.

Generally, different computing devices 1702(1)-(N) may each support oneor more VM instances 1704(1)-(N) that host workloads 1706, one or moreVM instances 1708(1)-(N) that host the workloads 1706, and one or moreVM instances 1710(1)-(N) that host the workloads 1706. Generally, thecomputing devices 1702(1)-(N) may be different models, generations,manufactures, and so forth. For example, the utilization data 1712 mayindicate resource consumption by different VM instance types 1704, 1708,1710 that run on different computing devices 1702, but host the sameworkload 1706.

In addition to utilizing resource-utilization data 1712 to determine anoptimized VM instance type 130 for the workload 1706, the optimizationservice 106 may further take into account the performance of theunderlying physical computing devices 1702(1)-(N). The performance mayvary based on the hardware differences of the different computingdevices 1702(1)-(N), such as different generations, models, and/orvendors of the chipset(s) in each of the computing devices 1702(1)-(N),such that actual performance of the computing devices 1702(1)-(N) variesbased on the hardware differences. For example, a computing device1702(1) that has a chipset from a newer generation may perform better,or have more data throughput, than a computing device 1702(2) with achipset from an older generation of chipsets. Thus, even if VM instance1704 is provided with, for example, the same number of vCPUs whenprovisioned on the different computing devices 1702(1) and 1702(2), theperformance for the VM instance 1704 hosting the workload 1706 may bebetter on the computing device 1702(1) compared to 1702(2) due tohardware differences (or improvements) in the physical resources of thecomputing devices 1702(1) and 1702(2). To help account for performancedifferences that result from physical hardware differences, theoptimization service 106 may map utilization data 1712 back to theunderlying physical computing resource that is consumed to get aperformance metric for the computing devices 1702(1)-(N).

For example, the utilization data 1712 may be placed into a chart wherethe device ID 1714 for different chipsets may be associated with ormapped to their utilization and performance. For example, the device IDs1714 may include a baseline ID indicating a benchmark of performance,and chipset models 1, 2, and 3 indicating different models, vendors,and/or generations of chipsets. Each chipset model may be associatedwith the number of CPUs 1718 for the chipset models, as well as thenumber of vCPUs 1716 being utilized by the VM instances 1706, 1708, and1710 on the different chipset models. Additionally, utilization data1720 (in this case, CPU usage) is associated with the different chipsetsand is in turn used to determine performance 1722. As illustrated,chipset model 1 is less performant than the baseline, but chipset model2 and 3 increase in performance. In this way, even if the same number ofCPUs and vCPUs are being utilized the performance by the chipset modelsmay differ due to the underlying hardware being more or less performantfor the throughput of data. To determine performance 1722, theoptimization service 106 may compare throughput of data, such as overallutilization 1720) for the respective compute type (e.g., CPU, memory,disk, GPU, network throughput, etc.), versus the baseline and/or acrossthe different chipset models. In this way, the optimization service 106may determine how performant one chipset model is compared to anotherchipset model.

Although illustrated as being CPU usage, the performance 1722 may bedetermined for one or more of the dimensions of compute (e.g., CPU,memory, disk, GPU, and network throughput) for the different chipsetmodels and/or device IDs 1714. For example, the optimization service 106may further determine, based on the utilization data 1712, howperformant each chipset model is (or other hardware device) for thedifferent dimensions of compute, and map back the performance for thoseadditional dimensions of compute to the respective chipset models. Inthis way, the performance 1722 for one or more dimensions of compute forthe underlying physical hardware may be determined using the utilizationdata 1712 in order to determine how to achieve best fit sizing for VMinstances and/or workloads 1706.

Once the performance 1722 is determine, the optimization service 106 maygenerate device mappings 1726 which generally map the device IDs 1714 ofthe underlying hardware resources back to the performance metrics 1724for one or more dimensions of compute. Thus, when the optimizationservice 106 is selecting a VM instance 114 and underlying hardwareresource to place a workload 136, the optimization service 106 mayselect the VM instance 114 at least partly based on the performancemetrics 1724.

As a specific example, a user account 242 may be hosting their workload1706 on a first VM instance type 130(1) that is supported by a firstchipset model 1714. However, that workload 1706 may be consuming toomuch CPU (and/or other computing resource) compared to a utilizationgoal or preference. The optimization service 106 may determine that asecond VM instance type 130(2) that is supported by a second chipsetmodel 1714 is more appropriate to host the workload 136 to achieve theutilization goal because, even if the second chipset model and second VMinstance type 130(2) have less CPUs 1718 and/or vCPUs 1716, the ratio ofperformance metrics 1724 between the first and second chipset models1714 may indicate that the second chipset model 1714 will still achievelower utilization, and the same or higher throughput, to support theworkload 1706. The optimization service 106 may select VM instance types130 based at least in part on the performance metrics 1724 for theunderlying computing resources of the supporting devices.

In this way, performance metrics 1724 may be assigned to the underlyingcomputing device 112 (or other computing resource) on which a VMinstance 116 is provisioned to help determine an optimized VM instancetype 130 based on the computing device that is to be utilized. In anexample where a workload 136 is migrated from a less compute-performantdevice 112 onto a more compute-performant device 112, the optimizationservice 106 may select a new VM instance type 130 based in part on aratio of the performance between the two devices 112. In this way, theoptimization service 106 may select a new VM instance type 130 that maynot need be allocated as much compute power of the morecompute-performance computing device 112, and drive down utilization.

FIG. 18 illustrates a flow diagram of an example method 1800 for using aperformance ratio between computing devices to determine that acomputing device has performance metrics such that, if a workload ishosted on the computing device, the resulting resource utilization rateof the workload will be within a desired utilization rate.

In some examples, the method 1800 may be performed by a systemcomprising a computing resource network of a service provider networkthat is managed by a service provider, where the computing resourcenetwork comprises a first physical server of a first server type, and asecond physical server of a second server type.

At 1802, the service provider network 102 may host a workload using thefirst physical server on behalf of a user account. At 1804, the serviceprovider network 102 may receive utilization data indicating an actualutilization rate of the first physical server by the workload.

At 1806, the service provider network 102 may determine that the actualutilization rate is different than a desired utilization rate specifiedfor the user account. At 1808, the service provider network 102 mayidentify a performance ratio between a first performance metric for thefirst physical server type and a second performance metric for thesecond physical server type. In some examples, the first performancemetric indicates an efficiency of the first physical server for hostingthe workload, and the second performance metric indicates an efficiencyof the second physical server for hosting the workload.

At 1810, the service provider network 102 may determine a predictedutilization rate for the second physical server to host the workloadbased at least in part on the actual utilization rate associated withthe first physical server and the performance ratio. For instance, theactual utilized rate may be multiplied by a ratio of the performancemetrics for the first and second physical servers.

At 1812, the service provider network 102 may determine that thepredicted utilization rate is within a threshold from the desiredutilization rate. At 1814, the service provider network 102 may migratethe workload to be hosted using the second physical server on behalf ofthe user account.

In some instances, the predicted utilization rate may be furtherdetermined based on details regarding the selected VM instance type 130.For instance, a difference in the number of vCPUs allocated to VMinstance types 130 may be different for the first and second physicalservers, and that may be factored in as a ratio for performance todetermine the predicted utilization rate.

FIG. 19 illustrates a flow diagram of an example method 1900 fordetermining a hardware device has performance metrics such that thehardware device is optimized to host a workload.

At 1902, the service provider network 102 may receive utilization dataindicating a first utilization rate of a resource type of a firsthardware device by a workload that is hosted on the first hardwaredevice.

At 1904, the service provider network 102 may determine that the firstutilization rate is different than a second utilization rate, whereinthe second utilization rate is specified for a user account associatedwith the workload.

At 1906, the service provider network 102 may determine, based at leastin part on a performance ratio associated with the first hardware deviceand a second hardware device, a third utilization rate of the resourcetype of the second hardware device by the workload, the thirdutilization rate being an expected utilization of the resource type ofthe second hardware device to host the workload.

At 1908, the service provider network 102 may determine that the secondhardware device is optimized to host the workload based at least in parton the third utilization rate being within a threshold from the secondutilization rate.

FIG. 20 illustrates a system-architecture diagram 2000 of an exampleenvironment in which an optimization service 106 of a service providernetwork 102 determines computationally compatible VM instances 114, anddeploys the computationally compatible VM instances 114 on a samecomputing device 112.

As illustrated, user devices 108(1) and 108(2) may submits respectiveworkload requests 2002(1) and 2002(2) to have workloads hosted in thecomputing-resource network 110. In some examples, the workload requests2002(1) and 2002(2) may be associated with a same user account 242, orwith different user accounts 242. The optimization component 126 maythen map the workload requests 2002(1)-(2) to respective workloadcategories 220(1) and 220(2) using the resource-utilization models224(1) and 224(2).

The optimization component 126 may then determine that the computationalbiases of the corresponding VM instances 114(1) and 114(2), and/or theworkload categories 220(1) and 220(2), are computationally complimentarysuch that it is advantageous to have a same computing device 112 hostthe two VM instances 114(1) and 114(2). For example, and as illustrated,the compute-dimension utilizations 2004 for each of theresource-utilization models 224(1) and 224(2) may be compatible suchthat the dimensions of compute utilized by workloads in the two workloadcategories 220(1) and 220(2) combine well to maximize the use of theresources provided by the computing device 112. As shown, fivedimensions of compute 2006, 2008, 2010, 2012, 2014 may complement eachother such that one dimension of compute 2006(1) for theresource-utilization model 224(1) may be relatively high, but the samedimension of compute 2006(2) for the resource-utilization model 224(2)may be relatively low.

As a specific example, the workload category 220(1) may utilize high CPUresources 2006(1), but low memory resources 2014(1), whereas theworkload category 220(2) may utilize low CPU resources 2006(2), but highmemory resources 2014(2), resulting in them being a complimentarycombination to share resources provided by the computing device 112. Iftwo VM instances 114 were placed on a computing device 112 that each hashigh CPU usage by low memory usage, then the computing device 112 wouldmax out on CPU used by the VM instances 114 while a large portion ofmemory sat idle and unused.

Accordingly, the optimization component 126 may identify and storeindications of workload categories 220 that have compute-dimensionutilization 2004 indicating that they are complimentary combinations.Additionally, or alternatively, the optimization component 126 may storeindications of VM instance types 130 that are computationallycomplimentary, and/or store indications of workloads 136 that arecomputationally complimentary in different examples.

In this way, when a VM instance 114(1) is to be placed on a computingdevice 112, the optimization component 126 may identify a computingdevice 112 that already has a computationally complimentary VM instance114(2) located thereon and place the VM instance 114(1) on thatcomputing device 112 to maximize the overall resource consumption.Although the compute-dimension utilization 2204 shows some or all of thecompute dimensions 2006-2014 being complimentary, in some instances, theworkload categories 220 and/or VM instance types 130 need only becomplimentary in one of the compute dimensions to warrant placement on asame computing device 112. In the illustrated example, the optimizationservice 106 may cause the VM instances 114(1) and 114(2) to be deployed2016(1)-(2) on the same computing device 112 to help maximize resourceconsumption for at least one dimension of compute.

Generally, computationally compatible workloads 136 and/or VM instancetypes 130 may be based on how well the compute-dimension utilization2004 matches up for at least one of the compute dimensions 2206-2014(e.g., one compute dimension, two compute dimensions, five computedimensions, etc.). In some examples, computationally compatibleworkloads 136 and/or VM instance types 130 may include combinations ofworkloads 136 and/or VM instance types 130 that, in combination, (i)achieve maximum utilization of the underlying computing device 112, (ii)achieve a desired or goal utilization of the underlying computing device112, and/or (iii) achieve a desired oversubscription of the underlyingcomputing device 112 to ensure efficient utilization of the underlyingresources of the computing device 112.

FIG. 21 is a system and network diagram that shows an illustrativeoperating environment that includes data centers a service providernetwork 102 that can be configured to implement aspects of thefunctionality described herein. The service provider network 102 canprovide computing resources, like VM instances and storage, on apermanent or an as-needed basis. Among other types of functionality, thecomputing resources 120 provided by the service provider network 102 maybe utilized to implement the various services described above. As alsodiscussed above, the computing resources provided by the serviceprovider network 102 can include various types of computing resources,such as data processing resources like VM instances, data storageresources, networking resources, data communication resources, networkservices, and the like.

Each type of computing resource provided by the service provider network102 can be general-purpose or can be available in a number of specificconfigurations. For example, data processing resources can be availableas physical computers or VM instances in a number of differentconfigurations. The VM instances can be configured to executeapplications, including web servers, application servers, media servers,database servers, gaming applications, some or all of the networkservices described above, and/or other types of programs. Data storageresources can include file storage devices, block storage devices, andthe like. The service provider network 102 can also be configured toprovide other types of computing resources not mentioned specificallyherein.

The computing resources provided by the service provider network 102 maybe enabled in one embodiment by one or more data centers 2104A-2104N(which might be referred to herein singularly as “a data center 2104” orin the plural as “the data centers 2104”). The data centers 2104 arefacilities utilized to house and operate computer systems and associatedcomponents. The data centers 2104 typically include redundant and backuppower, communications, cooling, and security systems. The data centers2104 can also be located in geographically disparate locations, orregions 2106. One illustrative embodiment for a data center 2104 thatcan be utilized to implement the technologies disclosed herein will bedescribed below with regard to FIG. 22.

The users 105 of the user devices 108 that utilize the service providernetwork 102 may access the computing resources provided by the serviceprovider network 102 over any wired and/or wireless network(s) 118,which can be a wide area communication network (“WAN”), such as theInternet, an intranet or an Internet service provider (“ISP”) network ora combination of such networks. For example, and without limitation, auser device 108 operated by a user 105 of the service provider network102 may be utilized to access the service provider network 102 by way ofthe network(s) 118. It should be appreciated that a local-area network(“LAN”), the Internet, or any other networking topology known in the artthat connects the data centers 2104 to remote clients and other userscan be utilized. It should also be appreciated that combinations of suchnetworks can also be utilized.

In some examples, the user devices 108(1)-(2) may submit their workloadrequests 2002(1)-(2) to the service provider network 102. Theoptimization service 106 may determine that the workloads 136 arecomputationally complimentary, and instruct the compute-managementservice 134 to place or deploy 2016(1)-(2) the workloads 136 on acomputing device 112 in the same data center 2104A.

FIG. 22 is a computing system diagram 2200 that illustrates oneconfiguration for a data center 2104 that implements aspects of thetechnologies disclosed herein. The example data center 2104 shown inFIG. 22 includes several server computers 2202A-2202F (which might bereferred to herein singularly as “a server computer 2202” or in theplural as “the server computers 2202”) for providing computing resources2204A-2204E. In some examples, the resources 2204 and/or servercomputers 2202 may include, be included in, or correspond to, thecomputing devices 112 described herein.

The server computers 2202 can be standard tower, rack-mount, or bladeserver computers configured appropriately for providing the computingresources described herein (illustrated in FIG. 22 as the computingresources 2204A-2204E). As mentioned above, the computing resourcesprovided by the service provider network 102 can be data processingresources such as VM instances or hardware computing systems, databaseclusters, computing clusters, storage clusters, data storage resources,database resources, networking resources, and others. Some of theservers 2202 can also be configured to execute a resource manager 2206capable of instantiating and/or managing the computing resources. In thecase of VM instances, for example, the resource manager 2206 can be ahypervisor or another type of program configured to enable the executionof multiple VM instances on a single server computer 2202. Servercomputers 2202 in the data center 2104 can also be configured to providenetwork services and other types of services.

In the example data center 2104 shown in FIG. 22, an appropriate LAN2208 is also utilized to interconnect the server computers 2202A-2202F.It should be appreciated that the configuration and network topologydescribed herein has been greatly simplified and that many morecomputing systems, software components, networks, and networking devicescan be utilized to interconnect the various computing systems disclosedherein and to provide the functionality described above. Appropriateload balancing devices or other types of network infrastructurecomponents can also be utilized for balancing a load between each of thedata centers 2104A-2104N, between each of the server computers2202A-2202F in each data center 2104, and, potentially, betweencomputing resources in each of the server computers 2202. It should beappreciated that the configuration of the data center 2104 describedwith reference to FIG. 22 is merely illustrative and that otherimplementations can be utilized.

The data center 2104 shown in FIG. 22 also includes a server computer2202F that can execute some or all of the software components describedabove. For example, and without limitation, the server computer 2202F(and the other server computers 2202) can generally be included in tothe computing devices 112 of FIG. 1 and be configured to executecomponents, including the components of the optimization service 106,the compute-management service 134, the computing-resource network 110,and/or the other software components described above. The servercomputer 2202F can also be configured to execute other components and/orto store data for providing some or all of the functionality describedherein. In this regard, it should be appreciated that the servicesillustrated in FIG. 22 as executing on the server computer 2202F canexecute on many other physical or virtual servers in the data centers2204 in various embodiments.

FIG. 23 shows an example computer architecture for a computer 2300capable of executing program components for implementing thefunctionality described above. The computer architecture shown in FIG.23 illustrates a conventional server computer, workstation, desktopcomputer, laptop, tablet, network appliance, e-reader, smartphone, orother computing device, and can be utilized to execute any of thesoftware components presented herein. In some examples, the servercomputer 2300 may correspond to, or be the same as or similar to, acomputing device 112 described in FIG. 1.

The computer 2300 includes a baseboard 2302, or “motherboard,” which isa printed circuit board to which a multitude of components or devicescan be connected by way of a system bus or other electricalcommunication paths. In one illustrative configuration, one or morecentral processing units (“CPUs”) 2304 operate in conjunction with achipset 2306. The CPUs 2304 can be standard programmable processors thatperform arithmetic and logical operations necessary for the operation ofthe computer 2300.

The CPUs 2304 perform operations by transitioning from one discrete,physical state to the next through the manipulation of switchingelements that differentiate between and change these states. Switchingelements generally include electronic circuits that maintain one of twobinary states, such as flip-flops, and electronic circuits that providean output state based on the logical combination of the states of one ormore other switching elements, such as logic gates. These basicswitching elements can be combined to create more complex logiccircuits, including registers, adders-subtractors, arithmetic logicunits, floating-point units, and the like.

The chipset 2306 provides an interface between the CPUs 2304 and theremainder of the components and devices on the baseboard 2302. Thechipset 2306 can provide an interface to a RAM 2308, used as the mainmemory in the computer 2300. The chipset 2306 can further provide aninterface to a computer-readable storage medium such as a read-onlymemory (“ROM”) 2310 or non-volatile RAM (“NVRAM”) for storing basicroutines that help to startup the computer 2300 and to transferinformation between the various components and devices. The ROM 2310 orNVRAM can also store other software components necessary for theoperation of the computer 2300 in accordance with the configurationsdescribed herein.

The computer 2300 can operate in a networked environment using logicalconnections to remote computing devices and computer systems through anetwork, such as the network 2208. The chipset 2306 can includefunctionality for providing network connectivity through a networkinterface controller (NIC) 2312, such as a gigabit Ethernet adapter. TheNIC 2312 is capable of connecting the computer 2300 to other computingdevices over the network 2208 (or 118). It should be appreciated thatmultiple NICs 2312 can be present in the computer 2300, connecting thecomputer to other types of networks and remote computer systems.

The computer 2300 can include storage 2314 (e.g., disk) that providesnon-volatile storage for the computer. The storage 2314 can consist ofone or more physical storage units. The storage 2314 can storeinformation by altering the magnetic characteristics of a particularlocation within a magnetic disk drive unit, the reflective or refractivecharacteristics of a particular location in an optical storage unit, orthe electrical characteristics of a particular capacitor, transistor, orother discrete component in a solid-state storage unit. Othertransformations of physical media are possible without departing fromthe scope and spirit of the present description, with the foregoingexamples provided only to facilitate this description. The computer 2300can further read information from the storage 2314 by detecting thephysical states or characteristics of one or more particular locationswithin the physical storage units.

In addition to the storage 2314 described above, the computer 2300 canhave access to other computer-readable storage media to store andretrieve information, such as program modules, data structures, or otherdata. It should be appreciated by those skilled in the art thatcomputer-readable storage media is any available media that provides forthe non-transitory storage of data and that can be accessed by thecomputer 2300. In some examples, the operations performed by the serviceprovider network 102, and or any components included therein, may besupported by one or more devices similar to computer 2300. Statedotherwise, some or all of the operations performed by the serviceprovider network 102, and or any components included therein, may beperformed by one or more computer devices 2300 operating in anetwork-based arrangement.

By way of example, and not limitation, computer-readable storage mediacan include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology. Computer-readable storage mediaincludes, but is not limited to, RAM, ROM, erasable programmable ROM(“EPROM”), electrically-erasable programmable ROM (“EEPROM”), flashmemory or other solid-state memory technology, compact disc ROM(“CD-ROM”), digital versatile disk (“DVD”), high definition DVD(“HD-DVD”), BLU-RAY, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired information ina non-transitory fashion.

The storage 2314 can store an operating system utilized to control theoperation of the computer 2300. According to one embodiment, theoperating system comprises the LINUX operating system. According toanother embodiment, the operating system comprises the WINDOWS SERVERoperating system from MICROSOFT Corporation of Redmond, Wash. Accordingto further embodiments, the operating system can comprise the UNIXoperating system or one of its variants. It should be appreciated thatother operating systems can also be utilized. The storage 2314 can storeother system or application programs and data utilized by the computer2300.

In one embodiment, the storage 2314, RAM 2308, ROM 2310, and/or othercomputer-readable storage media may be encoded with computer-executableinstructions which, when loaded into the computer 2300, transform thecomputer from a general-purpose computing system into a special-purposecomputer capable of implementing the embodiments described herein. Thesecomputer-executable instructions transform the computer 2300 byspecifying how the CPUs 2304 transition between states, as describedabove. According to one embodiment, the computer 2300 has access tocomputer-readable storage media storing computer-executable instructionswhich, when executed by the computer 2300, perform the varioustechniques described above. The computer 2300 can also includecomputer-readable storage media having instructions stored thereupon forperforming any of the other computer-implemented operations describedherein.

Generally, the computer 2300 may be an example of a computing device 112(and other computing devices, servers, etc.) described herein. TheCPU(s) 2304, RAM 2308, ROM 2310, storage 2314, bandwidth of the NIC2312, and/or other resources of the computer 230 may be allocated to oneor more different VM instances 114 as described herein based on the VMinstance types 130.

The computer 2300 can also include one or more input/output controllers2316 for receiving and processing input from a number of input devices,such as a keyboard, a mouse, a touchpad, a touch screen, an electronicstylus, or other type of input device. Similarly, an input/outputcontroller 2316 can provide output to a display, such as a computermonitor, a flat-panel display, a digital projector, a printer, or othertype of output device. It will be appreciated that the computer 2300might not include all of the components shown in FIG. 23, can includeother components that are not explicitly shown in FIG. 23, or mightutilize an architecture completely different than that shown in FIG. 23.

FIG. 24 illustrates a flow diagram of an example method 2400 fordeploying workloads on virtual computing resources that are supported bya same physical server based on the workloads being computationallycompatible. As described herein, a virtual computing resource maycomprise one or more of a VM instance 114, a virtual container, aprogram, and/or any other virtual representation.

At 2402, the service provider network 102 may receive a first request tosupport a first workload on behalf of a first user account using aphysical server in a service provider network, wherein the physicalserver supports workloads using a first resource type and a secondresource type.

At 2404, the service provider network 102 may receive first utilizationdata indicating a first resource amount of the first resource type and asecond resource amount of the second resource type utilized to supportthe first workload.

At 2406, the service provider network 102 may deploy the first workloadto a first virtual machine (VM) instance provisioned on the physicalserver, wherein the first virtual computing resource is optimized toutilize the first resource amount of the first resource type and thesecond resource amount of the second resource type of the physicalserver to support the first workload.

At 2408, the service provider network 102 may receive a second requestto support a second workload on behalf of a second user account of theservice provider network. At 2410, the service provider network 102 mayreceive second utilization data indicating a third resource amount ofthe first resource type and a fourth resource amount of the secondresource type utilized to support the second workload.

At 2412, the service provider network 102 may determine that the firstworkload and the second workload are complimentary workloads based onthe first resource amount utilized to support the first workload beinggreater than the second resource amount utilized to support the secondworkload, and the second resource amount utilized to support the firstworkload being less than the fourth resource amount utilized to supportthe second workload;

At 2412, the service provider network 102 may, based at least in part onthe first workload and the second workload being complimentaryworkloads, deploy the second workload to a second virtual computingresource provisioned on the physical server, wherein the second virtualcomputing resource is optimized to utilize the third resource amount andthe fourth resource amount to support the second workload.

In various examples, computationally compatible workloads 136 and/or VMinstance types 130 may be based on how well the compute-dimensionutilization 2004 matches up for at least one of the compute dimensions2206-2014 (e.g., one compute dimension, two compute dimensions, fivecompute dimensions, etc.). In some examples, computationally compatibleworkloads 136 and/or VM instance types 130 may include combinations ofworkloads 136 and/or VM instance types 130 that, in combination, (i)achieve maximum utilization of the underlying computing device 112, (ii)achieve a desired or goal utilization of the underlying computing device112, and/or (iii) achieve a desired oversubscription of the underlyingcomputing device 112 to ensure efficient utilization of the underlyingresources of the computing device 112.

FIG. 25 illustrates a flow diagram of an example method 2500 fordetermining that workloads are computationally compatible, and usingvirtual computing resources on a same hardware resource to host theworkloads. As described herein, a virtual computing resource maycomprise one or more of a VM instance 114, a virtual container, aprogram, and/or any other virtual representation.

At 2502, the service provider network 102 may host a first workload atleast partly using a first virtual machine (VM) instance that isprovisioned on a hardware resource of a service provider network. At2504, the service provider network 102 may receive first utilizationdata indicating a first amount of the hardware resource that is utilizedby the first workload.

At 2506, the service provider network 102 may receive second utilizationdata indicating a second amount of hardware resources that is utilizedby a second workload. At 2508, the service provider network 102 maydetermine, based at least in part on the first utilization data and thesecond utilization data, that the first workload and the second workloadare computationally complimentary to be hosted on a same hardwareresource.

At 2510, the service provider network 102 may provision a second virtualcomputing resource on the hardware resource. At 2512, the serviceprovider network 102 may host the second workload at least partly usinga second virtual computing resource.

FIG. 26 illustrates a flow diagram of an example method 2600 fordetermining to place workloads on virtual computing resources that areon a same hardware device based on the workloads belonging tocomputationally compatible workload categories. As described herein, avirtual computing resource may comprise one or more of a VM instance114, a virtual container, a program, and/or any other virtualrepresentation.

At 2602, the service provider network 102 may host a first workload atleast partly using a first virtual machine (VM) instance that isprovisioned on a hardware device of a service provider network. At 2604,the service provider network 102 may receive first utilization dataindicating a first resource amount of the hardware device utilized bythe first workload.

At 2606, the service provider network 102 may determine, based at leastin part on the first utilization data, that the first workloadcorresponds to a first workload category from a group of predefinedworkload categories.

As described throughout this application, determining that a workloadcorresponds to a workload category may comprise comparing resourceutilization for at least one of the computing resource types, andpotentially several of the computing resource types, to theresource-utilization models 224 for the workload categories 220. Theresource-utilization model 224 that is most similar across the one ormore dimensions of compute (e.g., the same, similar by a factor of somex amount, etc.) may be selected as the workload category 130 to whichthe workload 136 matches.

At 2608, the service provider network 102 may receive second utilizationdata indicating a second resource amount of that is utilized by a secondworkload. At 2610, the service provider network 102 may determine, basedat least in part on the second utilization data, that the secondworkload corresponds to a second workload category from the group ofpredefined workload categories.

At 2612, the service provider network 102 may determine that the firstworkload category and the second workload category are complimentaryworkload categories. At 2614, the service provider network 102 mayprovision a second virtual computing resource on the hardware device. At2616, the service provider network 102 may host the second workload atleast partly using a second virtual computing resource.

While the foregoing invention is described with respect to the specificexamples, it is to be understood that the scope of the invention is notlimited to these specific examples. Since other modifications andchanges varied to fit particular operating requirements and environmentswill be apparent to those skilled in the art, the invention is notconsidered limited to the example chosen for purposes of disclosure, andcovers all changes and modifications which do not constitute departuresfrom the true spirit and scope of this invention.

Although the application describes embodiments having specificstructural features and/or methodological acts, it is to be understoodthat the claims are not necessarily limited to the specific features oracts described. Rather, the specific features and acts are merelyillustrative some embodiments that fall within the scope of the claimsof the application.

What is claimed is:
 1. A system comprising: a computing resource networkof a service provider network that is managed by a service provider, thecomputing resource network comprising: a first physical server of afirst server type; and a second physical server of a second server type;one or more processors; and one or more computer-readable media storingcomputer-executable instructions that, when executed by the one or moreprocessors, cause the one or more processors to: host a workload usingthe first physical server on behalf of a user account; receiveutilization data indicating an actual utilization rate of the firstphysical server by the workload; determine that the actual utilizationrate is different than a desired utilization rate specified for the useraccount; identify a performance ratio between a first performance metricfor the first server type and a second performance metric for the secondserver type, wherein: the first performance metric indicates a firstefficiency of the first physical server for hosting the workload; andthe second performance metric indicates a second efficiency of thesecond physical server for hosting the workload; determine a predictedutilization rate for the second physical server to host the workloadbased at least in part on the actual utilization rate associated withthe first physical server and the performance ratio; determine that thepredicted utilization rate is within a threshold amount from the desiredutilization rate; and migrate the workload to be hosted using the secondphysical server on behalf of the user account.
 2. The system of claim 1,wherein: the first physical server includes a first number of virtualcentral processing units (vCPUs); the second physical server includes asecond number of vCPUs that is different than the first number of vCPUs;and determining the predicted utilization rate is further based at leastin part on the first number of vCPUs and the second number of vCPUs. 3.The system of claim 1, wherein the actual utilization rate comprises afirst actual utilization rate indicating utilization of a firstcomputing resource provided by the first physical server to theworkload, comprising further computer-executable instructions that, whenexecuted by the one or more processors, cause the one or more processorsto: determine, from the utilization data, a second actual utilizationrate indicating utilization by the workload of a first amount of asecond computing resource provided by the first physical server; anddetermine that the second physical server includes a second amount ofthe second computing resource that is greater than or equal to the firstamount utilized by the workload.
 4. The system of claim 1, comprisingfurther computer-executable instructions that, when executed by the oneor more processors, cause the one or more processors to: simulate a testworkload on a first test server that corresponds to the first servertype; determine the first performance metric based at least in part on afirst utilization rate of the first test server by the test workload;simulate the test workload on a second test server that corresponds tothe second server type; and determine the second performance metricbased at least in part on a second utilization rate of the second testserver by the test workload.
 5. A computer-implemented methodcomprising: receiving, at a service provider network, utilization dataindicating a first utilization rate of a resource type of a firsthardware device by a workload that is hosted on the first hardwaredevice; determining, by the service provider network, that the firstutilization rate is different than a second utilization rate, whereinthe second utilization rate is specified for a user account associatedwith the workload; determining, based at least in part on a performanceratio associated with the first hardware device and a second hardwaredevice, a third utilization rate of the resource type of the secondhardware device by the workload, the third utilization rate being anexpected utilization of the resource type of the second hardware deviceto host the workload; and determining that the second hardware device isoptimized to host the workload based at least in part on the thirdutilization rate being within a threshold amount from the secondutilization rate.
 6. The computer-implemented method of claim 5, furthercomprising determining the performance ratio based at least in part on afirst performance metric for the first hardware device and a secondperformance metric for the second hardware device, wherein: the firstperformance metric indicates a first efficiency of the first hardwaredevice for hosting the workload using the resource type; and the secondperformance metric indicates a second efficiency of the second hardwaredevice for hosting the workload using the resource type.
 7. Thecomputer-implemented method of claim 6, further comprising: simulating atest workload on a first test device that is of a same type as the firsthardware device; determining the first performance metric based at leastin part on a first test utilization rate of the first test device by thetest workload; simulating the test workload on a second test device thatis of a same type as the second hardware device; and determining thesecond performance metric based at least in part on a second testutilization rate of the second test device by the test workload.
 8. Thecomputer-implemented method of claim 5, wherein the resource typecomprises at least one of: a central processing unit (CPU) resourcetype; a memory resource type; a storage resource type; or a networkavailability resource type.
 9. The computer-implemented method of claim5, wherein: the first hardware device includes a first number of virtualcentral processing units (vCPUs); the second hardware device includes asecond number of vCPUs that is different than the first number of vCPUs;and determining the third utilization rate of the resource type of thesecond hardware device by the workload is further based at least in parton the first number of vCPUs and the second number of vCPUs.
 10. Thecomputer-implemented method of claim 5, wherein the resource typecomprises a first resource type utilized by the workload, furthercomprising: determining, from the utilization data, a fourth utilizationrate indicating utilization by the workload of a first amount of asecond resource type of the first hardware device; and determining thatthe second hardware device includes a second amount of the secondresource type that is greater than or equal to the first amount.
 11. Thecomputer-implemented method of claim 5, further comprising: providing,to the user account, recommendation data indicating that the secondhardware device is optimized to host the workload; and hosting theworkload on the second hardware device, wherein the second hardwaredevice is included in the service provider network.
 12. Thecomputer-implemented method of claim 5, wherein: receiving, from theuser account, an indication that the workload is being migrated frombeing supported by the first hardware device included in a computingresource network remote from the service provider network; and providingthe user account with an interface configured to receive the utilizationdata, wherein receiving the utilization data includes receiving, via theinterface, the utilization data.
 13. The computer-implemented method ofclaim 5, further comprising: receiving, from the user account, anoptimization goal indicating the second utilization rate that isspecified for the user account, wherein the optimization goal indicatesthat the second utilization rate is a desired utilization rate.
 14. Thecomputer-implemented method of claim 5, further comprising: deploying asoftware agent to the first hardware device, the software agent beingconfigured to collect the utilization data; and migrating the workloadfrom being hosted on the first hardware device to being hosted on thesecond hardware device on behalf of the user account, wherein theservice provider network includes the first hardware device and thesecond hardware device.
 15. A system comprising: one or more processors;and one or more computer-readable media storing computer-executableinstructions that, when executed by the one or more processors, causethe one or more processors to: receive, at a service provider network,utilization data indicating a first utilization rate of a resource typeof a first hardware device by a workload that is hosted on the firsthardware device; determine, by the service provider network, that thefirst utilization rate is different than a second utilization rate,wherein the second utilization rate is specified for a user accountassociated with the workload; determine, based at least in part on aperformance ratio associated with the first hardware device and a secondhardware device, a third utilization rate of the resource type of thesecond hardware device by the workload, the third utilization rate beingan expected utilization of the resource type of the second hardwaredevice to host the workload; determine that the second hardware deviceis optimized to host the workload based at least in part on the thirdutilization rate being within a threshold amount from the secondutilization rate; and host the workload on the second hardware device inthe service provider network on behalf of the user account.
 16. Thesystem of claim 15, comprising further computer-executable instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to determine the performance ratio based at least in part ona first performance metric for the first hardware device and a secondperformance metric for the second hardware device, wherein: the firstperformance metric indicates a first efficiency of the first hardwaredevice for hosting the workload using the resource type; and the secondperformance metric indicates a second efficiency of the second hardwaredevice for hosting the workload using the resource type.
 17. The systemof claim 16, comprising further computer-executable instructions that,when executed by the one or more processors, cause the one or moreprocessors to: simulate a test workload on a first test device that isof a same type as the first hardware device; determine the firstperformance metric based at least in part on a first test utilizationrate of the first test device by the test workload; simulate the testworkload on a second test device that is of a same type as the secondhardware device; and determine the second performance metric based atleast in part on a second test utilization rate of the second testdevice by the test workload.
 18. The system of claim 15, wherein: thefirst hardware device includes a first number of virtual centralprocessing units (vCPUs); the second hardware device includes a secondnumber of vCPUs that is different than the first number of vCPUs,determining the third utilization rate of the resource type of thesecond hardware device by the workload is further based at least in parton the first number of vCPUs and the second number of vCPUs.
 19. Thesystem of claim 15, wherein the resource type comprises a first resourcetype utilized by the workload, comprising further computer-executableinstructions that, when executed by the one or more processors, causethe one or more processors to: determine, from the utilization data, afourth utilization rate indicating utilization by the workload of afirst amount of a second resource type of the first hardware device; anddetermine that the second hardware device includes a second amount ofthe second resource type that is greater than or equal to the firstamount.
 20. The system of claim 15, comprising furthercomputer-executable instructions that, when executed by the one or moreprocessors, cause the one or more processors to: receive, from the useraccount, an indication that the workload is being migrated from beingsupported by the first hardware device included in a computing resourcenetwork remote from the service provider network; and provide the useraccount with an interface configured to receive the utilization data,wherein receiving the utilization data includes receiving, via theinterface, the utilization data.